This paper presents a new framework for object-based classification of high-resolution hyperspectral data.This multi-step framework is based on multi-resolution segmentation(MRS)and Random Forest classifier(RFC)algori...This paper presents a new framework for object-based classification of high-resolution hyperspectral data.This multi-step framework is based on multi-resolution segmentation(MRS)and Random Forest classifier(RFC)algorithms.The first step is to determine of weights of the input features while using the object-based approach with MRS to processing such images.Given the high number of input features,an automatic method is needed for estimation of this parameter.Moreover,we used the Variable Importance(VI),one of the outputs of the RFC,to determine the importance of each image band.Then,based on this parameter and other required parameters,the image is segmented into some homogenous regions.Finally,the RFC is carried out based on the characteristics of segments for converting them into meaningful objects.The proposed method,as well as,the conventional pixel-based RFC and Support Vector Machine(SVM)method was applied to three different hyperspectral data-sets with various spectral and spatial characteristics.These data were acquired by the HyMap,the Airborne Prism Experiment(APEX),and the Compact Airborne Spectrographic Imager(CASI)hyperspectral sensors.The experimental results show that the proposed method is more consistent for land cover mapping in various areas.The overall classification accuracy(OA),obtained by the proposed method was 95.48,86.57,and 84.29%for the HyMap,the APEX,and the CASI datasets,respectively.Moreover,this method showed better efficiency in comparison to the spectralbased classifications because the OAs of the proposed method was 5.67 and 3.75%higher than the conventional RFC and SVM classifiers,respectively.展开更多
Efficient and accurate access to coastal land cover information is of great significance for marine disaster prevention and mitigation.Although the popular and common sensors of land resource satellites provide free a...Efficient and accurate access to coastal land cover information is of great significance for marine disaster prevention and mitigation.Although the popular and common sensors of land resource satellites provide free and valuable images to map the land cover,coastal areas often encounter significant cloud cover,especially in tropical areas,which makes the classification in those areas non-ideal.To solve this problem,we proposed a framework of combining medium-resolution optical images and synthetic aperture radar(SAR)data with the recently popular object-based image analysis(OBIA)method and used the Landsat Operational Land Imager(OLI)and Phased Array type L-band Synthetic Aperture Radar(PALSAR)images acquired in Singapore in 2017 as a case study.We designed experiments to confirm two critical factors of this framework:one is the segmentation scale that determines the average object size,and the other is the classification feature.Accuracy assessments of the land cover indicated that the optimal segmentation scale was between 40 and 80,and the features of the combination of OLI and SAR resulted in higher accuracy than any individual features,especially in areas with cloud cover.Based on the land cover generated by this framework,we assessed the vulnerability of the marine disasters of Singapore in 2008 and 2017 and found that the high-vulnerability areas mainly located in the southeast and increased by 118.97 km2 over the past decade.To clarify the disaster response plan for different geographical environments,we classified risk based on altitude and distance from shore.The newly increased high-vulnerability regions within 4 km offshore and below 30 m above sea level are at high risk;these regions may need to focus on strengthening disaster prevention construction.This study serves as a typical example of using remote sensing techniques for the vulnerability assessment of marine disasters,especially those in cloudy coastal areas.展开更多
The Baltic Sea is a brackish, mediterranean sea located in the middle latitudes of Europe. It is seasonally covered with ice. The ice covered areas during a typical winter are the Bothnian Bay, the Gulf of Finnland an...The Baltic Sea is a brackish, mediterranean sea located in the middle latitudes of Europe. It is seasonally covered with ice. The ice covered areas during a typical winter are the Bothnian Bay, the Gulf of Finnland and the Gulf of Riga. Sea ice plays an important role in dynamic and thermodynamic processes and also has a strong impact on the heat budget of the sea. Also a large part of transport goes by sea, and there is a need to create ice charts to make the marine transport safe. Because of high cloudiness in winter season and small amount of light in the northern part of the Baltic Sea, radar data are the most important remote sensing source of sea ice information. The main goal of the following studies is classification of the Baltic sea ice cover using radar data. The ENVISAT ASAR （Advanced Synthetic Aperture Radar） acquires data in five different modes. In the following studies ASAR Wide Swath Mode data were used. The Wide Swath Mode, using the ScanSAR technique provides medium resolution images （150 m） over a swath of 405 kin, at HH or VV polarization. In following work data from February 13th, February 24th and April 6th, 2011, representing three different sea ice situations were chosen. OBIA （object-based image analysis） methods and texture parameters were used to create sea ice extent and sea ice concentration charts. Based on object-based methods, it can separate single sea ice floes within the ice pack and calculate more accurately sea ice concentration.展开更多
This paper aims to assess the ways in which multi-resolution object-based classification methods can be used to group urban environments made up of a mixture of buildings, sub-elements such as car parks, roads, shades...This paper aims to assess the ways in which multi-resolution object-based classification methods can be used to group urban environments made up of a mixture of buildings, sub-elements such as car parks, roads, shades and pavements and foliage such as grass and trees. This involves using both unmanned aerial vehicles (UAVs) which provide high-resolution mosaic Orthoimages and generate a Digital Surface Model (DSM). For the study area chosen for this paper, 400 Orthoimages with a spatial resolution of 7 cm each were used to build the Orthoimages and DSM, which were georeferenced using well distributed network of ground control points (GCPs) of 12 reference points (RMSE = 8 cm). As these were combined with onboard RTK-GNSS-enabled 2-frequency receivers, they were able to provide absolute block orientation which had a similar accuracy range if the data had been collected by traditional indirect sensor orientation. Traditional indirect sensor orientation involves the GNSS receiver in the UAV receiving a differential signal from the base station through a communication link. This allows for the precise position of the UAV to be established, as the RTK uses correction, allowing position, velocity, altitude and heading to tracked, as well as the measurement of raw sensor data. By assessing the results of the confusion matrices, it can be seen that the overall accuracy of the object-oriented classification was 84.37%. This has an overall Kappa of 0.74 and the data that had poor classification accuracy included shade, parking lots and concrete pavements. These had a producer accuracy (precision) of 81%, 74% and 74% respectively, while lakes and solar panels each scored 100% in comparison, meaning that they had good classification accuracy.展开更多
Alpine wetlands are very sensitive to global change, have great impacts on the hydrological condition of rivers, and are closely related to peoples＇ living in lower reaches. It is essential to monitor alpine wetland ...Alpine wetlands are very sensitive to global change, have great impacts on the hydrological condition of rivers, and are closely related to peoples＇ living in lower reaches. It is essential to monitor alpine wetland changes to appropriately manage and protect wetland resources; however, it is quite difficult to accurately extract such information from remote sensing images due to spectral confusion and arduous field verification. In this study, we identified different wetland types in the Damqu River Basin located in the Yangze River source region from Landsat remote sensing data using the object-based method. In order to ensure the interpretation accuracy of wetland, a digital elevation model （DEM） and its derived data （slope, aspect）, Normalized Difference Vegetation Index （NDVI）, Normalized Difference Water Index （NDWI）, and Kauth-Thomas transformation were considered as the components of the spectral characteristics of wetland types. The spectral characteristics, texture features and spatial structure characteristics of each wetland type were comprehensively analyzed based on the success of image segmentation. The extraction rules for each wetland type were established by determining the thresholds of the spatial, texture and spectral attributes of typical parameter layers according to their histogram statistics. The classification accuracy was assessed using error matrixes and field survey verification data. According to the accuracy assessment, the total accuracy of image classification was 89%.展开更多
Suburban greenhouses with intensive agricultural productivity have increasingly influenced the daily diet and vegetable supply in Chinese cities.With their enormous input of fertilizers and pesticides,greenhouses have...Suburban greenhouses with intensive agricultural productivity have increasingly influenced the daily diet and vegetable supply in Chinese cities.With their enormous input of fertilizers and pesticides,greenhouses have considerably changed the local soil quality and environmental risk factors.The ability to obtain timely and accurate information regarding the spatial distribution of greenhouses could make an important contribution to local agricultural management and soil protection.This paper attempts to present a practical framework for extracting suburban greenhouses,integrating remote sensing data from Landsat-8 and object-oriented classification.Inheritance classification was implemented,and various properties,including texture and neighborhood features in addition to spectral information,were investigated through the popular random forest technique for feature selection prior to SVM classification to improve the mapping accuracy.The results demonstrated that object-based classification incorporating non-spectral features yielded a significant improvement compared with the classification results obtained using only the spectral information in traditional per-pixel classification.Both the producer’s and user’s accuracy were higher than 85%for greenhouse identification.Although it remained a challenge to completely distinguish greenhouses from sparse plants,the final greenhouse map indicated that the proposed object-based classification scheme,providing multiple feature selections and multi-scale analysis,yielded worthwhile information when applied to a continuous series of the freely available Landsat-8 imagery data.展开更多
Many researches have been performed comparing object-based classification (OBC) and pixel-based classification (PBC), particularly in classifying high-resolution satellite images. VNREDSat-1 is the first optical remot...Many researches have been performed comparing object-based classification (OBC) and pixel-based classification (PBC), particularly in classifying high-resolution satellite images. VNREDSat-1 is the first optical remote sensing satellite of Vietnam with resolution of 2.5 m (Panchromatic) and 10 m (Multispectral). The objective of this research is to compare two classification approaches using VNREDSat-1 image for mapping mangrove forest in Vien An Dong commune, Ngoc Hien district, Ca Mau province. ISODATA algorithm (in PBC method) and membership function classifier (in OBC method) were chosen to classify the same image. The results show that the overall accuracies of OBC and PBC are 73% and 62.16% respectively, and OBC solved the “salt and pepper” which is the main issue of PBC as well. Therefore, OBC is supposed to be the better approach to classify VNREDSat-1 for mapping mangrove forest in Ngoc Hien commune.展开更多
There is increasing interest in developing automatic procedures to segment landscapes into soil spatial entities that replace conventional, expensive manual procedures for delineating and classifying soils. Geographic...There is increasing interest in developing automatic procedures to segment landscapes into soil spatial entities that replace conventional, expensive manual procedures for delineating and classifying soils. Geographic object-based image analysis(GEOBIA)partitions remote sensing imagery or digital elevation models into homogeneous image objects based on image segmentation. We used an object-based methodology for the detailed delineation and classification of soil types using digital maps of topography and vegetation as soil covariates, based on the Random Forests(RF) classifier. We compared the object-based method's results with those of a pixel-based classification using the same classifier. We used 18 digital elevation model derivatives and 5 remote sensing indices that were related to vegetation cover and soil. Using 171 soil profiles with their associated environmental variable values,the RF method was used to identify the most important soil type predictors for use in the segmentation process. A stack of rastergeodatasets corresponding to the selected predictors was segmented using a multi-resolution segmentation algorithm, which resulted in homogeneous objects related to soil types. These objects were further classified as soil types using the same method, RF. We also conducted a pixel-based classification using the same classifier and soil profiles, and the resulting maps were assessed in terms of their accuracy using 30% of the soil profiles for validation. We found that GEOBIA was an effective method for soil type mapping, and was superior to the pixel-based approach. The optimized object-based soil map had an overall accuracy of 58%, which was 10% higher than that of the optimized pixel-based map.展开更多
Accurate crop distribution mapping is required for crop yield prediction and field management. Due to rapid progress in remote sensing technology, fine spatial resolution(FSR) remotely sensed imagery now offers great ...Accurate crop distribution mapping is required for crop yield prediction and field management. Due to rapid progress in remote sensing technology, fine spatial resolution(FSR) remotely sensed imagery now offers great opportunities for mapping crop types in great detail. However, within-class variance can hamper attempts to discriminate crop classes at fine resolutions. Multi-temporal FSR remotely sensed imagery provides a means of increasing crop classification from FSR imagery, although current methods do not exploit the available information fully. In this research, a novel Temporal Sequence Object-based Convolutional Neural Network(TS-OCNN) was proposed to classify agricultural crop type from FSR image time-series. An object-based CNN(OCNN) model was adopted in the TS-OCNN to classify images at the object level(i.e., segmented objects or crop parcels), thus, maintaining the precise boundary information of crop parcels. The combination of image time-series was first utilized as the input to the OCNN model to produce an ‘original’ or baseline classification. Then the single-date images were fed automatically into the deep learning model scene-by-scene in order of image acquisition date to increase successively the crop classification accuracy. By doing so, the joint information in the FSR multi-temporal observations and the unique individual information from the single-date images were exploited comprehensively for crop classification. The effectiveness of the proposed approach was investigated using multitemporal SAR and optical imagery, respectively, over two heterogeneous agricultural areas. The experimental results demonstrated that the newly proposed TS-OCNN approach consistently increased crop classification accuracy, and achieved the greatest accuracies(82.68% and 87.40%) in comparison with state-of-the-art benchmark methods, including the object-based CNN(OCNN)(81.63% and85.88%), object-based image analysis(OBIA)(78.21% and 84.83%), and standard pixel-wise CNN(79.18%and 82.90%). The proposed approach is the first known attempt to explore simultaneously the joint information from image time-series with the unique information from single-date images for crop classification using a deep learning framework. The TS-OCNN, therefore, represents a new approach for agricultural landscape classification from multi-temporal FSR imagery. Besides, it is readily generalizable to other landscapes(e.g., forest landscapes), with a wide application prospect.展开更多
Object-based classification differentiates forest gaps from canopies at large regional scale by using remote sensing data. To study the segmentation and classification processes of object-based forest gaps classificat...Object-based classification differentiates forest gaps from canopies at large regional scale by using remote sensing data. To study the segmentation and classification processes of object-based forest gaps classification at a regional scale, we sampled a natural secondary forest in northeast China at Maoershan Experimental Forest Farm.Airborne light detection and ranging(LiDAR; 3.7 points/m2) data were collected as the original data source and the canopy height model(CHM) and topographic dataset were extracted from the LiDAR data. The accuracy of objectbased forest gaps classification depends on previous segmentation. Thus our first step was to define 10 different scale parameters in CHM image segmentation. After image segmentation, the machine learning classification method was used to classify three kinds of object classes, namely,forest gaps, tree canopies, and others. The common support vector machine(SVM) classifier with the radial basis function kernel(RBF) was first adopted to test the effect of classification features(vegetation height features and some typical topographic features) on forest gap classification.Then the different classifiers(KNN, Bayes, decision tree,and SVM with linear kernel) were further adopted to compare the effect of classifiers on machine learning forest gaps classification. Segmentation accuracy and classification accuracy were evaluated by using Mo¨ller's method and confusion metrics, respectively. The scale parameter had a significant effect on object-based forest gap segmentation and classification. Classification accuracies at different scales revealed that there were two optimal scales(10 and 20) that provided similar accuracy, with the scale of 10 yielding slightly greater accuracy than 20. The accuracy of the classification by using combination of height features and SVM classifier with linear kernel was91% at the optimal scale parameter of 10, and it was highest comparing with other classification classifiers, such as SVM RBF(90%), Decision Tree(90%), Bayes(90%),or KNN(87%). The classifiers had no significant effect on forest gap classification, but the fewer parameters in the classifier equation and higher speed of operation probably lead to a higher accuracy of final classifications. Our results confirm that object-based classification can extract forest gaps at a large regional scale with appropriate classification features and classifiers using LiDAR data. We note, however, that final satisfaction of forest gap classification depends on the determination of optimal scale(s) of segmentation.展开更多
An object-based approach is proposed for land cover classification using optimal polarimetric parameters.The ability to identify targets is effectively enhanced by the integration of SAR and optical images.The innovat...An object-based approach is proposed for land cover classification using optimal polarimetric parameters.The ability to identify targets is effectively enhanced by the integration of SAR and optical images.The innovation of the presented method can be summarized in the following two main points:①estimating polarimetric parameters(H-A-Alpha decomposition)through the optical image as a driver;②a multi-resolution segmentation based on the optical image only is deployed to refine classification results.The proposed method is verified by using Sentinel-1/2 datasets over the Bakersfield area,California.The results are compared against those from pixel-based SVM classification using the ground truth from the National Land Cover Database(NLCD).A detailed accuracy assessment complied with seven classes shows that the proposed method outperforms the conventional approach by around 10%,with an overall accuracy of 92.6%over regions with rich texture.展开更多
Conventional image classification based on pixels hinders the possibilities to obtain information contained in images, while modern object-based classification methods increase the acquisition of information about the...Conventional image classification based on pixels hinders the possibilities to obtain information contained in images, while modern object-based classification methods increase the acquisition of information about the object and the context in which it is inserted in the image. The objective of this study was to investigate the performance of different classification methods for land cover mapping in the vicinity of the Alto Ribeira Tourist State Park, a Brazilian Atlantic rainforest area. Two classification methods were tested, including i） a hybrid per-pixel classification using the image processing software ERDAS Imagine version 9.1 and ii） an object-based classification using the software eCognition version 5. In the first method, six different classes were established, while in the second method, another two classes were established in addition to the six classes in the first method. Accuracy assessment of the classification results presented showed that the object-based classification with a Kappa index value of 0.8687 outperformed the per-pixel classification with a Kappa index value of 0.2224. Application of the user＇s knowledge during the object-based classification process achieved the desired quality; therefore, the use of inter-relationships between objects, superelasses, subclasses, and neighboring classes were critical to improving the efficiency of land cover classification.展开更多
With the deterioration of the environment,it is imperative to protect coastal wetlands.Using multi-source remote sensing data and object-based hierarchical classification to classify coastal wetlands is an effective m...With the deterioration of the environment,it is imperative to protect coastal wetlands.Using multi-source remote sensing data and object-based hierarchical classification to classify coastal wetlands is an effective method.The object-based hierarchical classification using remote sensing indices(OBH-RSI)for coastal wetland is proposed to achieve fine classification of coastal wetland.First,the original categories are divided into four groups according to the category characteristics.Second,the training and test maps of each group are extracted according to the remote sensing indices.Third,four groups are passed through the classifier in order.Finally,the results of the four groups are combined to get the final classification result map.The experimental results demonstrate that the overall accuracy,average accuracy and kappa coefficient of the proposed strategy are over 94%using the Yellow River Delta dataset.展开更多
Urban tree species provide various essential ecosystem services in cities,such as regulating urban temperatures,reducing noise,capturing carbon,and mitigating the urban heat island effect.The quality of these services...Urban tree species provide various essential ecosystem services in cities,such as regulating urban temperatures,reducing noise,capturing carbon,and mitigating the urban heat island effect.The quality of these services is influenced by species diversity,tree health,and the distribution and the composition of trees.Traditionally,data on urban trees has been collected through field surveys and manual interpretation of remote sensing images.In this study,we evaluated the effectiveness of multispectral airborne laser scanning(ALS)data in classifying 24 common urban roadside tree species in Espoo,Finland.Tree crown structure information,intensity features,and spectral data were used for classification.Eight different machine learning algorithms were tested,with the extra trees(ET)algorithm performing the best,achieving an overall accuracy of 71.7%using multispectral LiDAR data.This result highlights that integrating structural and spectral information within a single framework can improve the classification accuracy.Future research will focus on identifying the most important features for species classification and developing algorithms with greater efficiency and accuracy.展开更多
Purpose:Interdisciplinary research has become a critical approach to addressing complex societal,economic,technological,and environmental challenges,driving innovation and integrating scientific knowledge.While interd...Purpose:Interdisciplinary research has become a critical approach to addressing complex societal,economic,technological,and environmental challenges,driving innovation and integrating scientific knowledge.While interdisciplinarity indicators are widely used to evaluate research performance,the impact of classification granularity on these assessments remains underexplored.Design/methodology/approach:This study investigates how different levels of classification granularity-macro,meso,and micro-affect the evaluation of interdisciplinarity in research institutes.Using a dataset of 262 institutes from four major German non-university organizations(FHG,HGF,MPG,WGL)from 2018 to 2022,we examine inconsistencies in interdisciplinarity across levels,analyze ranking changes,and explore the influence of institutional fields and research focus(applied vs.basic).Findings:Our findings reveal significant inconsistencies in interdisciplinarity across classification levels,with rankings varying substantially.Notably,the Fraunhofer Society(FHG),which performs well at the macro level,experiences significant ranking declines at meso and micro levels.Normalizing interdisciplinarity by research field confirmed that these declines persist.The research focus of institutes,whether applied,basic,or mixed,does not significantly explain the observed ranking dynamics.Research limitations:This study has only considered the publication-based dimension of institutional interdisciplinarity and has not explored other aspects.Practical implications:The findings provide insights for policymakers,research managers,and scholars to better interpret interdisciplinarity metrics and support interdisciplinary research effectively.Originality/value:This study underscores the critical role of classification granularity in interdisciplinarity assessment and emphasizes the need for standardized approaches to ensure robust and fair evaluations.展开更多
Preservation of the crops depends on early and accurate detection of pests on crops as they cause several diseases decreasing crop production and quality. Several deep-learning techniques have been applied to overcome...Preservation of the crops depends on early and accurate detection of pests on crops as they cause several diseases decreasing crop production and quality. Several deep-learning techniques have been applied to overcome the issue of pest detection on crops. We have developed the YOLOCSP-PEST model for Pest localization and classification. With the Cross Stage Partial Network (CSPNET) backbone, the proposed model is a modified version of You Only Look Once Version 7 (YOLOv7) that is intended primarily for pest localization and classification. Our proposed model gives exceptionally good results under conditions that are very challenging for any other comparable models especially conditions where we have issues with the luminance and the orientation of the images. It helps farmers working out on their crops in distant areas to determine any infestation quickly and accurately on their crops which helps in the quality and quantity of the production yield. The model has been trained and tested on 2 datasets namely the IP102 data set and a local crop data set on both of which it has shown exceptional results. It gave us a mean average precision (mAP) of 88.40% along with a precision of 85.55% and a recall of 84.25% on the IP102 dataset meanwhile giving a mAP of 97.18% on the local data set along with a recall of 94.88% and a precision of 97.50%. These findings demonstrate that the proposed model is very effective in detecting real-life scenarios and can help in the production of crops improving the yield quality and quantity at the same time.展开更多
The cleanliness of seed cotton plays a critical role in the pre-treatment of cotton textiles,and the removal of impurity during the harvesting process directly determines the quality and market value of cotton textile...The cleanliness of seed cotton plays a critical role in the pre-treatment of cotton textiles,and the removal of impurity during the harvesting process directly determines the quality and market value of cotton textiles.By fusing band combination optimization with deep learning,this study aims to achieve more efficient and accurate detection of film impurities in seed cotton on the production line.By applying hyperspectral imaging and a one-dimensional deep learning algorithm,we detect and classify impurities in seed cotton after harvest.The main categories detected include pure cotton,conveyor belt,film covering seed cotton,and film adhered to the conveyor belt.The proposed method achieves an impurity detection rate of 99.698%.To further ensure the feasibility and practical application potential of this strategy,we compare our results against existing mainstream methods.In addition,the model shows excellent recognition performance on pseudo-color images of real samples.With a processing time of 11.764μs per pixel from experimental data,it shows a much improved speed requirement while maintaining the accuracy of real production lines.This strategy provides an accurate and efficient method for removing impurities during cotton processing.展开更多
Myocardial perfusion imaging(MPI),which uses single-photon emission computed tomography(SPECT),is a well-known estimating tool for medical diagnosis,employing the classification of images to show situations in coronar...Myocardial perfusion imaging(MPI),which uses single-photon emission computed tomography(SPECT),is a well-known estimating tool for medical diagnosis,employing the classification of images to show situations in coronary artery disease(CAD).The automatic classification of SPECT images for different techniques has achieved near-optimal accuracy when using convolutional neural networks(CNNs).This paper uses a SPECT classification framework with three steps:1)Image denoising,2)Attenuation correction,and 3)Image classification.Image denoising is done by a U-Net architecture that ensures effective image denoising.Attenuation correction is implemented by a convolution neural network model that can remove the attenuation that affects the feature extraction process of classification.Finally,a novel multi-scale diluted convolution(MSDC)network is proposed.It merges the features extracted in different scales and makes the model learn the features more efficiently.Three scales of filters with size 3×3 are used to extract features.All three steps are compared with state-of-the-art methods.The proposed denoising architecture ensures a high-quality image with the highest peak signal-to-noise ratio(PSNR)value of 39.7.The proposed classification method is compared with the five different CNN models,and the proposed method ensures better classification with an accuracy of 96%,precision of 87%,sensitivity of 87%,specificity of 89%,and F1-score of 87%.To demonstrate the importance of preprocessing,the classification model was analyzed without denoising and attenuation correction.展开更多
Diagnosing cardiac diseases relies heavily on electrocardiogram(ECG)analysis,but detecting myocardial infarction-related arrhythmias remains challenging due to irregular heartbeats and signal variations.Despite advanc...Diagnosing cardiac diseases relies heavily on electrocardiogram(ECG)analysis,but detecting myocardial infarction-related arrhythmias remains challenging due to irregular heartbeats and signal variations.Despite advancements in machine learning,achieving both high accuracy and low computational cost for arrhythmia classification remains a critical issue.Computer-aided diagnosis systems can play a key role in early detection,reducing mortality rates associated with cardiac disorders.This study proposes a fully automated approach for ECG arrhythmia classification using deep learning and machine learning techniques to improve diagnostic accuracy while minimizing processing time.The methodology consists of three stages:1)preprocessing,where ECG signals undergo noise reduction and feature extraction;2)feature Identification,where deep convolutional neural network(CNN)blocks,combined with data augmentation and transfer learning,extract key parameters;3)classification,where a hybrid CNN-SVM model is employed for arrhythmia recognition.CNN-extracted features were fed into a binary support vector machine(SVM)classifier,and model performance was assessed using five-fold cross-validation.Experimental findings demonstrated that the CNN2 model achieved 85.52%accuracy,while the hybrid CNN2-SVM approach significantly improved accuracy to 97.33%,outperforming conventional methods.This model enhances classification efficiency while reducing computational complexity.The proposed approach bridges the gap between accuracy and processing speed in ECG arrhythmia classification,offering a promising solution for real-time clinical applications.Its superior performance compared to nonlinear classifiers highlights its potential for improving automated cardiac diagnosis.展开更多
In the era of precision medicine,the classification of diabetes mellitus has evolved beyond the traditional categories.Various classification methods now account for a multitude of factors,including variations in spec...In the era of precision medicine,the classification of diabetes mellitus has evolved beyond the traditional categories.Various classification methods now account for a multitude of factors,including variations in specific genes,type ofβ-cell impairment,degree of insulin resistance,and clinical characteristics of metabolic profiles.Improved classification methods enable healthcare providers to formulate blood glucose management strategies more precisely.Applying these updated classification systems,will assist clinicians in further optimising treatment plans,including targeted drug therapies,personalized dietary advice,and specific exercise plans.Ultimately,this will facilitate stricter blood glucose control,minimize the risks of hypoglycaemia and hyperglycaemia,and reduce long-term complications associated with diabetes.展开更多
文摘This paper presents a new framework for object-based classification of high-resolution hyperspectral data.This multi-step framework is based on multi-resolution segmentation(MRS)and Random Forest classifier(RFC)algorithms.The first step is to determine of weights of the input features while using the object-based approach with MRS to processing such images.Given the high number of input features,an automatic method is needed for estimation of this parameter.Moreover,we used the Variable Importance(VI),one of the outputs of the RFC,to determine the importance of each image band.Then,based on this parameter and other required parameters,the image is segmented into some homogenous regions.Finally,the RFC is carried out based on the characteristics of segments for converting them into meaningful objects.The proposed method,as well as,the conventional pixel-based RFC and Support Vector Machine(SVM)method was applied to three different hyperspectral data-sets with various spectral and spatial characteristics.These data were acquired by the HyMap,the Airborne Prism Experiment(APEX),and the Compact Airborne Spectrographic Imager(CASI)hyperspectral sensors.The experimental results show that the proposed method is more consistent for land cover mapping in various areas.The overall classification accuracy(OA),obtained by the proposed method was 95.48,86.57,and 84.29%for the HyMap,the APEX,and the CASI datasets,respectively.Moreover,this method showed better efficiency in comparison to the spectralbased classifications because the OAs of the proposed method was 5.67 and 3.75%higher than the conventional RFC and SVM classifiers,respectively.
基金Supported by the National Key Research and Development Program of China(No.2016YFC1402003)the CAS Earth Big Data Science Project(No.XDA19060303)the Innovation Project of the State Key Laboratory of Resources and Environmental Information System(No.O88RAA01YA)
文摘Efficient and accurate access to coastal land cover information is of great significance for marine disaster prevention and mitigation.Although the popular and common sensors of land resource satellites provide free and valuable images to map the land cover,coastal areas often encounter significant cloud cover,especially in tropical areas,which makes the classification in those areas non-ideal.To solve this problem,we proposed a framework of combining medium-resolution optical images and synthetic aperture radar(SAR)data with the recently popular object-based image analysis(OBIA)method and used the Landsat Operational Land Imager(OLI)and Phased Array type L-band Synthetic Aperture Radar(PALSAR)images acquired in Singapore in 2017 as a case study.We designed experiments to confirm two critical factors of this framework:one is the segmentation scale that determines the average object size,and the other is the classification feature.Accuracy assessments of the land cover indicated that the optimal segmentation scale was between 40 and 80,and the features of the combination of OLI and SAR resulted in higher accuracy than any individual features,especially in areas with cloud cover.Based on the land cover generated by this framework,we assessed the vulnerability of the marine disasters of Singapore in 2008 and 2017 and found that the high-vulnerability areas mainly located in the southeast and increased by 118.97 km2 over the past decade.To clarify the disaster response plan for different geographical environments,we classified risk based on altitude and distance from shore.The newly increased high-vulnerability regions within 4 km offshore and below 30 m above sea level are at high risk;these regions may need to focus on strengthening disaster prevention construction.This study serves as a typical example of using remote sensing techniques for the vulnerability assessment of marine disasters,especially those in cloudy coastal areas.
文摘The Baltic Sea is a brackish, mediterranean sea located in the middle latitudes of Europe. It is seasonally covered with ice. The ice covered areas during a typical winter are the Bothnian Bay, the Gulf of Finnland and the Gulf of Riga. Sea ice plays an important role in dynamic and thermodynamic processes and also has a strong impact on the heat budget of the sea. Also a large part of transport goes by sea, and there is a need to create ice charts to make the marine transport safe. Because of high cloudiness in winter season and small amount of light in the northern part of the Baltic Sea, radar data are the most important remote sensing source of sea ice information. The main goal of the following studies is classification of the Baltic sea ice cover using radar data. The ENVISAT ASAR （Advanced Synthetic Aperture Radar） acquires data in five different modes. In the following studies ASAR Wide Swath Mode data were used. The Wide Swath Mode, using the ScanSAR technique provides medium resolution images （150 m） over a swath of 405 kin, at HH or VV polarization. In following work data from February 13th, February 24th and April 6th, 2011, representing three different sea ice situations were chosen. OBIA （object-based image analysis） methods and texture parameters were used to create sea ice extent and sea ice concentration charts. Based on object-based methods, it can separate single sea ice floes within the ice pack and calculate more accurately sea ice concentration.
文摘This paper aims to assess the ways in which multi-resolution object-based classification methods can be used to group urban environments made up of a mixture of buildings, sub-elements such as car parks, roads, shades and pavements and foliage such as grass and trees. This involves using both unmanned aerial vehicles (UAVs) which provide high-resolution mosaic Orthoimages and generate a Digital Surface Model (DSM). For the study area chosen for this paper, 400 Orthoimages with a spatial resolution of 7 cm each were used to build the Orthoimages and DSM, which were georeferenced using well distributed network of ground control points (GCPs) of 12 reference points (RMSE = 8 cm). As these were combined with onboard RTK-GNSS-enabled 2-frequency receivers, they were able to provide absolute block orientation which had a similar accuracy range if the data had been collected by traditional indirect sensor orientation. Traditional indirect sensor orientation involves the GNSS receiver in the UAV receiving a differential signal from the base station through a communication link. This allows for the precise position of the UAV to be established, as the RTK uses correction, allowing position, velocity, altitude and heading to tracked, as well as the measurement of raw sensor data. By assessing the results of the confusion matrices, it can be seen that the overall accuracy of the object-oriented classification was 84.37%. This has an overall Kappa of 0.74 and the data that had poor classification accuracy included shade, parking lots and concrete pavements. These had a producer accuracy (precision) of 81%, 74% and 74% respectively, while lakes and solar panels each scored 100% in comparison, meaning that they had good classification accuracy.
基金funded by National Natural Science Foundation of China (Grant No.40901057)National Basic Research Program of China (Grant No.2010CB951704)
文摘Alpine wetlands are very sensitive to global change, have great impacts on the hydrological condition of rivers, and are closely related to peoples＇ living in lower reaches. It is essential to monitor alpine wetland changes to appropriately manage and protect wetland resources; however, it is quite difficult to accurately extract such information from remote sensing images due to spectral confusion and arduous field verification. In this study, we identified different wetland types in the Damqu River Basin located in the Yangze River source region from Landsat remote sensing data using the object-based method. In order to ensure the interpretation accuracy of wetland, a digital elevation model （DEM） and its derived data （slope, aspect）, Normalized Difference Vegetation Index （NDVI）, Normalized Difference Water Index （NDWI）, and Kauth-Thomas transformation were considered as the components of the spectral characteristics of wetland types. The spectral characteristics, texture features and spatial structure characteristics of each wetland type were comprehensively analyzed based on the success of image segmentation. The extraction rules for each wetland type were established by determining the thresholds of the spatial, texture and spectral attributes of typical parameter layers according to their histogram statistics. The classification accuracy was assessed using error matrixes and field survey verification data. According to the accuracy assessment, the total accuracy of image classification was 89%.
基金The authors are grateful for the support of the National Ecological Survey and Evaluation(2000-2010)under the auspices of the Remote Sensing Program of the Chinese Ministry of Environmental Protection(No.STSN-05-11).
文摘Suburban greenhouses with intensive agricultural productivity have increasingly influenced the daily diet and vegetable supply in Chinese cities.With their enormous input of fertilizers and pesticides,greenhouses have considerably changed the local soil quality and environmental risk factors.The ability to obtain timely and accurate information regarding the spatial distribution of greenhouses could make an important contribution to local agricultural management and soil protection.This paper attempts to present a practical framework for extracting suburban greenhouses,integrating remote sensing data from Landsat-8 and object-oriented classification.Inheritance classification was implemented,and various properties,including texture and neighborhood features in addition to spectral information,were investigated through the popular random forest technique for feature selection prior to SVM classification to improve the mapping accuracy.The results demonstrated that object-based classification incorporating non-spectral features yielded a significant improvement compared with the classification results obtained using only the spectral information in traditional per-pixel classification.Both the producer’s and user’s accuracy were higher than 85%for greenhouse identification.Although it remained a challenge to completely distinguish greenhouses from sparse plants,the final greenhouse map indicated that the proposed object-based classification scheme,providing multiple feature selections and multi-scale analysis,yielded worthwhile information when applied to a continuous series of the freely available Landsat-8 imagery data.
文摘Many researches have been performed comparing object-based classification (OBC) and pixel-based classification (PBC), particularly in classifying high-resolution satellite images. VNREDSat-1 is the first optical remote sensing satellite of Vietnam with resolution of 2.5 m (Panchromatic) and 10 m (Multispectral). The objective of this research is to compare two classification approaches using VNREDSat-1 image for mapping mangrove forest in Vien An Dong commune, Ngoc Hien district, Ca Mau province. ISODATA algorithm (in PBC method) and membership function classifier (in OBC method) were chosen to classify the same image. The results show that the overall accuracies of OBC and PBC are 73% and 62.16% respectively, and OBC solved the “salt and pepper” which is the main issue of PBC as well. Therefore, OBC is supposed to be the better approach to classify VNREDSat-1 for mapping mangrove forest in Ngoc Hien commune.
基金supported by the Romanian Government through a doctoral scholarship
文摘There is increasing interest in developing automatic procedures to segment landscapes into soil spatial entities that replace conventional, expensive manual procedures for delineating and classifying soils. Geographic object-based image analysis(GEOBIA)partitions remote sensing imagery or digital elevation models into homogeneous image objects based on image segmentation. We used an object-based methodology for the detailed delineation and classification of soil types using digital maps of topography and vegetation as soil covariates, based on the Random Forests(RF) classifier. We compared the object-based method's results with those of a pixel-based classification using the same classifier. We used 18 digital elevation model derivatives and 5 remote sensing indices that were related to vegetation cover and soil. Using 171 soil profiles with their associated environmental variable values,the RF method was used to identify the most important soil type predictors for use in the segmentation process. A stack of rastergeodatasets corresponding to the selected predictors was segmented using a multi-resolution segmentation algorithm, which resulted in homogeneous objects related to soil types. These objects were further classified as soil types using the same method, RF. We also conducted a pixel-based classification using the same classifier and soil profiles, and the resulting maps were assessed in terms of their accuracy using 30% of the soil profiles for validation. We found that GEOBIA was an effective method for soil type mapping, and was superior to the pixel-based approach. The optimized object-based soil map had an overall accuracy of 58%, which was 10% higher than that of the optimized pixel-based map.
基金supported by the Strategic Priority Research Program of the Chinese Academy of Sciences (XDA28070503)the National Key Research and Development Program of China(2021YFD1500100)+2 种基金the Open Fund of State Laboratory of Information Engineering in Surveying,Mapping and Remote Sensing,Wuhan University (20R04)Land Observation Satellite Supporting Platform of National Civil Space Infrastructure Project(CASPLOS-CCSI)a PhD studentship ‘‘Deep Learning in massive area,multi-scale resolution remotely sensed imagery”(EAA7369),sponsored by Lancaster University and Ordnance Survey (the national mapping agency of Great Britain)。
文摘Accurate crop distribution mapping is required for crop yield prediction and field management. Due to rapid progress in remote sensing technology, fine spatial resolution(FSR) remotely sensed imagery now offers great opportunities for mapping crop types in great detail. However, within-class variance can hamper attempts to discriminate crop classes at fine resolutions. Multi-temporal FSR remotely sensed imagery provides a means of increasing crop classification from FSR imagery, although current methods do not exploit the available information fully. In this research, a novel Temporal Sequence Object-based Convolutional Neural Network(TS-OCNN) was proposed to classify agricultural crop type from FSR image time-series. An object-based CNN(OCNN) model was adopted in the TS-OCNN to classify images at the object level(i.e., segmented objects or crop parcels), thus, maintaining the precise boundary information of crop parcels. The combination of image time-series was first utilized as the input to the OCNN model to produce an ‘original’ or baseline classification. Then the single-date images were fed automatically into the deep learning model scene-by-scene in order of image acquisition date to increase successively the crop classification accuracy. By doing so, the joint information in the FSR multi-temporal observations and the unique individual information from the single-date images were exploited comprehensively for crop classification. The effectiveness of the proposed approach was investigated using multitemporal SAR and optical imagery, respectively, over two heterogeneous agricultural areas. The experimental results demonstrated that the newly proposed TS-OCNN approach consistently increased crop classification accuracy, and achieved the greatest accuracies(82.68% and 87.40%) in comparison with state-of-the-art benchmark methods, including the object-based CNN(OCNN)(81.63% and85.88%), object-based image analysis(OBIA)(78.21% and 84.83%), and standard pixel-wise CNN(79.18%and 82.90%). The proposed approach is the first known attempt to explore simultaneously the joint information from image time-series with the unique information from single-date images for crop classification using a deep learning framework. The TS-OCNN, therefore, represents a new approach for agricultural landscape classification from multi-temporal FSR imagery. Besides, it is readily generalizable to other landscapes(e.g., forest landscapes), with a wide application prospect.
基金financially supported by grant from National Natural Science Foundation of China(No.31300533)
文摘Object-based classification differentiates forest gaps from canopies at large regional scale by using remote sensing data. To study the segmentation and classification processes of object-based forest gaps classification at a regional scale, we sampled a natural secondary forest in northeast China at Maoershan Experimental Forest Farm.Airborne light detection and ranging(LiDAR; 3.7 points/m2) data were collected as the original data source and the canopy height model(CHM) and topographic dataset were extracted from the LiDAR data. The accuracy of objectbased forest gaps classification depends on previous segmentation. Thus our first step was to define 10 different scale parameters in CHM image segmentation. After image segmentation, the machine learning classification method was used to classify three kinds of object classes, namely,forest gaps, tree canopies, and others. The common support vector machine(SVM) classifier with the radial basis function kernel(RBF) was first adopted to test the effect of classification features(vegetation height features and some typical topographic features) on forest gap classification.Then the different classifiers(KNN, Bayes, decision tree,and SVM with linear kernel) were further adopted to compare the effect of classifiers on machine learning forest gaps classification. Segmentation accuracy and classification accuracy were evaluated by using Mo¨ller's method and confusion metrics, respectively. The scale parameter had a significant effect on object-based forest gap segmentation and classification. Classification accuracies at different scales revealed that there were two optimal scales(10 and 20) that provided similar accuracy, with the scale of 10 yielding slightly greater accuracy than 20. The accuracy of the classification by using combination of height features and SVM classifier with linear kernel was91% at the optimal scale parameter of 10, and it was highest comparing with other classification classifiers, such as SVM RBF(90%), Decision Tree(90%), Bayes(90%),or KNN(87%). The classifiers had no significant effect on forest gap classification, but the fewer parameters in the classifier equation and higher speed of operation probably lead to a higher accuracy of final classifications. Our results confirm that object-based classification can extract forest gaps at a large regional scale with appropriate classification features and classifiers using LiDAR data. We note, however, that final satisfaction of forest gap classification depends on the determination of optimal scale(s) of segmentation.
基金The National Key Research and Development Program of China(No.2018YFC0407900)The National Natural Science Foundation of China(No.41774003)+2 种基金The Natural Science Foundation of Jiangsu Province(No.BK20171432)The Fundamental Research Funds for the Central Universities(No.2018B177142019B60714)。
文摘An object-based approach is proposed for land cover classification using optimal polarimetric parameters.The ability to identify targets is effectively enhanced by the integration of SAR and optical images.The innovation of the presented method can be summarized in the following two main points:①estimating polarimetric parameters(H-A-Alpha decomposition)through the optical image as a driver;②a multi-resolution segmentation based on the optical image only is deployed to refine classification results.The proposed method is verified by using Sentinel-1/2 datasets over the Bakersfield area,California.The results are compared against those from pixel-based SVM classification using the ground truth from the National Land Cover Database(NLCD).A detailed accuracy assessment complied with seven classes shows that the proposed method outperforms the conventional approach by around 10%,with an overall accuracy of 92.6%over regions with rich texture.
基金Supported by the Sa o Paulo Research Foundation (FAPESP), Brazil
文摘Conventional image classification based on pixels hinders the possibilities to obtain information contained in images, while modern object-based classification methods increase the acquisition of information about the object and the context in which it is inserted in the image. The objective of this study was to investigate the performance of different classification methods for land cover mapping in the vicinity of the Alto Ribeira Tourist State Park, a Brazilian Atlantic rainforest area. Two classification methods were tested, including i） a hybrid per-pixel classification using the image processing software ERDAS Imagine version 9.1 and ii） an object-based classification using the software eCognition version 5. In the first method, six different classes were established, while in the second method, another two classes were established in addition to the six classes in the first method. Accuracy assessment of the classification results presented showed that the object-based classification with a Kappa index value of 0.8687 outperformed the per-pixel classification with a Kappa index value of 0.2224. Application of the user＇s knowledge during the object-based classification process achieved the desired quality; therefore, the use of inter-relationships between objects, superelasses, subclasses, and neighboring classes were critical to improving the efficiency of land cover classification.
基金supported by the Beijing Natural Science Foundation(No.JQ20021)the National Natural Science Foundation of China(Nos.61922013,61421001 and U1833203)the Remote Sensing Monitoring Project of Geographical Elements in Shandong Yellow River Delta National Nature Reserve。
文摘With the deterioration of the environment,it is imperative to protect coastal wetlands.Using multi-source remote sensing data and object-based hierarchical classification to classify coastal wetlands is an effective method.The object-based hierarchical classification using remote sensing indices(OBH-RSI)for coastal wetland is proposed to achieve fine classification of coastal wetland.First,the original categories are divided into four groups according to the category characteristics.Second,the training and test maps of each group are extracted according to the remote sensing indices.Third,four groups are passed through the classifier in order.Finally,the results of the four groups are combined to get the final classification result map.The experimental results demonstrate that the overall accuracy,average accuracy and kappa coefficient of the proposed strategy are over 94%using the Yellow River Delta dataset.
文摘Urban tree species provide various essential ecosystem services in cities,such as regulating urban temperatures,reducing noise,capturing carbon,and mitigating the urban heat island effect.The quality of these services is influenced by species diversity,tree health,and the distribution and the composition of trees.Traditionally,data on urban trees has been collected through field surveys and manual interpretation of remote sensing images.In this study,we evaluated the effectiveness of multispectral airborne laser scanning(ALS)data in classifying 24 common urban roadside tree species in Espoo,Finland.Tree crown structure information,intensity features,and spectral data were used for classification.Eight different machine learning algorithms were tested,with the extra trees(ET)algorithm performing the best,achieving an overall accuracy of 71.7%using multispectral LiDAR data.This result highlights that integrating structural and spectral information within a single framework can improve the classification accuracy.Future research will focus on identifying the most important features for species classification and developing algorithms with greater efficiency and accuracy.
文摘Purpose:Interdisciplinary research has become a critical approach to addressing complex societal,economic,technological,and environmental challenges,driving innovation and integrating scientific knowledge.While interdisciplinarity indicators are widely used to evaluate research performance,the impact of classification granularity on these assessments remains underexplored.Design/methodology/approach:This study investigates how different levels of classification granularity-macro,meso,and micro-affect the evaluation of interdisciplinarity in research institutes.Using a dataset of 262 institutes from four major German non-university organizations(FHG,HGF,MPG,WGL)from 2018 to 2022,we examine inconsistencies in interdisciplinarity across levels,analyze ranking changes,and explore the influence of institutional fields and research focus(applied vs.basic).Findings:Our findings reveal significant inconsistencies in interdisciplinarity across classification levels,with rankings varying substantially.Notably,the Fraunhofer Society(FHG),which performs well at the macro level,experiences significant ranking declines at meso and micro levels.Normalizing interdisciplinarity by research field confirmed that these declines persist.The research focus of institutes,whether applied,basic,or mixed,does not significantly explain the observed ranking dynamics.Research limitations:This study has only considered the publication-based dimension of institutional interdisciplinarity and has not explored other aspects.Practical implications:The findings provide insights for policymakers,research managers,and scholars to better interpret interdisciplinarity metrics and support interdisciplinary research effectively.Originality/value:This study underscores the critical role of classification granularity in interdisciplinarity assessment and emphasizes the need for standardized approaches to ensure robust and fair evaluations.
基金supported by King Saud University,Riyadh,Saudi Arabia,through the Researchers Supporting Project under Grant RSPD2025R697.
文摘Preservation of the crops depends on early and accurate detection of pests on crops as they cause several diseases decreasing crop production and quality. Several deep-learning techniques have been applied to overcome the issue of pest detection on crops. We have developed the YOLOCSP-PEST model for Pest localization and classification. With the Cross Stage Partial Network (CSPNET) backbone, the proposed model is a modified version of You Only Look Once Version 7 (YOLOv7) that is intended primarily for pest localization and classification. Our proposed model gives exceptionally good results under conditions that are very challenging for any other comparable models especially conditions where we have issues with the luminance and the orientation of the images. It helps farmers working out on their crops in distant areas to determine any infestation quickly and accurately on their crops which helps in the quality and quantity of the production yield. The model has been trained and tested on 2 datasets namely the IP102 data set and a local crop data set on both of which it has shown exceptional results. It gave us a mean average precision (mAP) of 88.40% along with a precision of 85.55% and a recall of 84.25% on the IP102 dataset meanwhile giving a mAP of 97.18% on the local data set along with a recall of 94.88% and a precision of 97.50%. These findings demonstrate that the proposed model is very effective in detecting real-life scenarios and can help in the production of crops improving the yield quality and quantity at the same time.
基金supported in part by the Six Talent Peaks Project in Jiangsu Province under Grant 013040315in part by the China Textile Industry Federation Science and Technology Guidance Project under Grant 2017107+1 种基金in part by the National Natural Science Foundation of China under Grant 31570714in part by the China Scholarship Council under Grant 202108320290。
文摘The cleanliness of seed cotton plays a critical role in the pre-treatment of cotton textiles,and the removal of impurity during the harvesting process directly determines the quality and market value of cotton textiles.By fusing band combination optimization with deep learning,this study aims to achieve more efficient and accurate detection of film impurities in seed cotton on the production line.By applying hyperspectral imaging and a one-dimensional deep learning algorithm,we detect and classify impurities in seed cotton after harvest.The main categories detected include pure cotton,conveyor belt,film covering seed cotton,and film adhered to the conveyor belt.The proposed method achieves an impurity detection rate of 99.698%.To further ensure the feasibility and practical application potential of this strategy,we compare our results against existing mainstream methods.In addition,the model shows excellent recognition performance on pseudo-color images of real samples.With a processing time of 11.764μs per pixel from experimental data,it shows a much improved speed requirement while maintaining the accuracy of real production lines.This strategy provides an accurate and efficient method for removing impurities during cotton processing.
基金the Research Grant of Kwangwoon University in 2024.
文摘Myocardial perfusion imaging(MPI),which uses single-photon emission computed tomography(SPECT),is a well-known estimating tool for medical diagnosis,employing the classification of images to show situations in coronary artery disease(CAD).The automatic classification of SPECT images for different techniques has achieved near-optimal accuracy when using convolutional neural networks(CNNs).This paper uses a SPECT classification framework with three steps:1)Image denoising,2)Attenuation correction,and 3)Image classification.Image denoising is done by a U-Net architecture that ensures effective image denoising.Attenuation correction is implemented by a convolution neural network model that can remove the attenuation that affects the feature extraction process of classification.Finally,a novel multi-scale diluted convolution(MSDC)network is proposed.It merges the features extracted in different scales and makes the model learn the features more efficiently.Three scales of filters with size 3×3 are used to extract features.All three steps are compared with state-of-the-art methods.The proposed denoising architecture ensures a high-quality image with the highest peak signal-to-noise ratio(PSNR)value of 39.7.The proposed classification method is compared with the five different CNN models,and the proposed method ensures better classification with an accuracy of 96%,precision of 87%,sensitivity of 87%,specificity of 89%,and F1-score of 87%.To demonstrate the importance of preprocessing,the classification model was analyzed without denoising and attenuation correction.
文摘Diagnosing cardiac diseases relies heavily on electrocardiogram(ECG)analysis,but detecting myocardial infarction-related arrhythmias remains challenging due to irregular heartbeats and signal variations.Despite advancements in machine learning,achieving both high accuracy and low computational cost for arrhythmia classification remains a critical issue.Computer-aided diagnosis systems can play a key role in early detection,reducing mortality rates associated with cardiac disorders.This study proposes a fully automated approach for ECG arrhythmia classification using deep learning and machine learning techniques to improve diagnostic accuracy while minimizing processing time.The methodology consists of three stages:1)preprocessing,where ECG signals undergo noise reduction and feature extraction;2)feature Identification,where deep convolutional neural network(CNN)blocks,combined with data augmentation and transfer learning,extract key parameters;3)classification,where a hybrid CNN-SVM model is employed for arrhythmia recognition.CNN-extracted features were fed into a binary support vector machine(SVM)classifier,and model performance was assessed using five-fold cross-validation.Experimental findings demonstrated that the CNN2 model achieved 85.52%accuracy,while the hybrid CNN2-SVM approach significantly improved accuracy to 97.33%,outperforming conventional methods.This model enhances classification efficiency while reducing computational complexity.The proposed approach bridges the gap between accuracy and processing speed in ECG arrhythmia classification,offering a promising solution for real-time clinical applications.Its superior performance compared to nonlinear classifiers highlights its potential for improving automated cardiac diagnosis.
文摘In the era of precision medicine,the classification of diabetes mellitus has evolved beyond the traditional categories.Various classification methods now account for a multitude of factors,including variations in specific genes,type ofβ-cell impairment,degree of insulin resistance,and clinical characteristics of metabolic profiles.Improved classification methods enable healthcare providers to formulate blood glucose management strategies more precisely.Applying these updated classification systems,will assist clinicians in further optimising treatment plans,including targeted drug therapies,personalized dietary advice,and specific exercise plans.Ultimately,this will facilitate stricter blood glucose control,minimize the risks of hypoglycaemia and hyperglycaemia,and reduce long-term complications associated with diabetes.
