Mapping regional spatial patterns of coral reef geomorphology provides the primary information to understand the constructive processes in the reef ecosystem. However, this work is challenged by the pixel-based image ...Mapping regional spatial patterns of coral reef geomorphology provides the primary information to understand the constructive processes in the reef ecosystem. However, this work is challenged by the pixel-based image classification method for its comparatively low accuracy. In this paper, an object-based image analysis(OBIA)method was presented to map intra-reef geomorphology of coral reefs in the Xisha Islands, China using Landsat 8satellite imagery. Following the work of the Millennium Coral Reef Mapping Project, a regional reef class hierarchy with ten geomorphic classes was first defined. Then, incorporating the hierarchical concept and integrating the spectral and additional spatial information such as context, shape and contextual relationships, a large-scale geomorphic map was produced by OBIA with accuracies generally more than 80%. Although the robustness of OBIA has been validated in the applications of coral reef mapping from individual reefs to reef system in this paper, further work is still required to improve its transferability.展开更多
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
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
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
Gully feature mapping is an indispensable prerequisite for the motioning and control of gully erosion which is a widespread natural hazard. The increasing availability of high-resolution Digital Elevation Model(DEM) a...Gully feature mapping is an indispensable prerequisite for the motioning and control of gully erosion which is a widespread natural hazard. The increasing availability of high-resolution Digital Elevation Model(DEM) and remote sensing imagery, combined with developed object-based methods enables automatic gully feature mapping. But still few studies have specifically focused on gully feature mapping on different scales. In this study, an object-based approach to two-level gully feature mapping, including gully-affected areas and bank gullies, was developed and tested on 1-m DEM and Worldview-3 imagery of a catchment in the Chinese Loess Plateau. The methodology includes a sequence of data preparation, image segmentation, metric calculation, and random forest based classification. The results of the two-level mapping were based on a random forest model after investigating the effects of feature selection and class-imbalance problem. Results show that the segmentation strategy adopted in this paper which considers the topographic information and optimal parameter combination can improve the segmentation results. The distribution of the gully-affected area is closely related to topographic information, however, the spectral features are more dominant for bank gully mapping. The highest overall accuracy of the gully-affected area mapping was 93.06% with four topographic features. The highest overall accuracy of bank gully mapping is 78.5% when all features are adopted. The proposed approach is a creditable option for hierarchical mapping of gully feature information, which is suitable for the application in hily Loess Plateau region.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
The detection of impervious surface (IS) in heterogeneous urban areas is one of the most challenging tasks in urban remote sensing. One of the limitations in IS detection at the parcel level is the lack of sufficient ...The detection of impervious surface (IS) in heterogeneous urban areas is one of the most challenging tasks in urban remote sensing. One of the limitations in IS detection at the parcel level is the lack of sufficient training data. In this study, a generic model of spatial distribution of roof materials is considered to overcome this limitation. A generic model that is based on spectral, spatial and textural information which is extracted from available training data is proposed. An object-based approach is used to extract the information inherent in the image. Furthermore, linear discriminant analysis is used for dimensionality reduction and to discriminate between different spatial, spectral and textural attributes. The generic model is composed of a discriminant function based on linear combinations of the predictor variables that provide the best discrimination among the groups. The discriminate analysis result shows that of the 54 attributes extracted from the WorldView-2 image, only 13 attributes related to spatial, spectral and textural information are useful for discriminating different roof materials. Finally, this model is applied to different WorldView-2 images from different areas and proves that this model has good potential to predict roof materials from the WorldView-2 images without using training data.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
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
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
As forest is of great significance for our whole development and the sustainable plan is so focus on it. It is very urgent for us to have the whole distribution,stock volume and other related information about that. S...As forest is of great significance for our whole development and the sustainable plan is so focus on it. It is very urgent for us to have the whole distribution,stock volume and other related information about that. So the forest inventory program is on our schedule. Aiming at dealing with the problem in extraction of dominant tree species,we tested the highly hot method-object-based analysis. Based on the ALOS image data,we combined multi-resolution in e Cognition software and fuzzy classification algorithm. Through analyzing the segmentation results,we basically extract the spruce,the pine,the birch and the oak of the study area. Both the spectral and spatial characteristics were derived from those objects,and with the help of GLCM,we got the differences of each species. We use confusion matrix to do the Classification accuracy assessment compared with the actual ground data and this method showed a comparatively good precision as 87% with the kappa coefficient 0. 837.展开更多
The majority of the population and economic activity of the northern half of Vietnam is clustered in the Red River Delta and about half of the country’s rice production takes place here. There are significant problem...The majority of the population and economic activity of the northern half of Vietnam is clustered in the Red River Delta and about half of the country’s rice production takes place here. There are significant problems associated with its geographical position and the intensive exploitation of resources by an overabundant population (population density of 962 inhabitants/km2). Some thirty years after the economic liberalization and the opening of the country to international markets, agricultural land use patterns in the Red River Delta, particularly in the coastal area, have undergone many changes. Remote sensing is a particularly powerful tool in processing and providing spatial information for monitoring land use changes. The main methodological objective is to find a solution to process the many heterogeneous coastal land use parameters, so as to describe it in all its complexity, specifically by making use of the latest European satellite data (Sentinel-2). This complexity is due to local variations in ecological conditions, but also to anthropogenic factors that directly and indirectly influence land use dynamics. The methodological objective was to develop a new Geographic Object-based Image Analysis (GEOBIA) approach for mapping coastal areas using Sentinel-2 data and Landsat 8. By developing a new segmentation, accuracy measure, in this study was determined that segmentation accuracies decrease with increasing segmentation scales and that the negative impact of under-segmentation errors significantly increases at a large scale. An Estimation of Scale Parameter (ESP) tool was then used to determine the optimal segmentation parameter values. A popular machine learning algorithms (Random Forests-RFs) is used. For all classifications algorithm, an increase in overall accuracy was observed with the full synergistic combination of available data sets.展开更多
This paper proposes an unequal error protection（UEP）coding method to improve the transmission performance of three-dimensional（3D）audio based on expanding window fountain（EWF）.Different from other transmissions ...This paper proposes an unequal error protection（UEP）coding method to improve the transmission performance of three-dimensional（3D）audio based on expanding window fountain（EWF）.Different from other transmissions with equal error protection（EEP）when transmitting the 3D audio objects.An approach of extracting the important audio object is presented,and more protection is given to more important audio object and comparatively less protection is given to the normal audio objects.Objective and subjective experiments have shown that the proposed UEP method achieves better performance than equal error protection method,while the bits error rates（BER）of the important audio object can decrease from 10^（–3） to 10^（–4）,and the subjective quality of UEP is better than that of EEP by 14%.展开更多
Changing climate has a great impact on northern area of Pakistan＇s environment and is more prone to environmental changes impacts than rest of the country due to its high elevation. However, melting glaciers effect n...Changing climate has a great impact on northern area of Pakistan＇s environment and is more prone to environmental changes impacts than rest of the country due to its high elevation. However, melting glaciers effect not only the local environment but also the whole country with frequent and heavy floods. Remote sensing （RS） from Satellites and Airplanes used in Geographical Information Systems （GIS） are technologies that can aid in understanding the on-going environmental processes. Furthermore, help researchers to observe, understand, forecast and suggest response to changes that occur. It can be natural disasters or man-made disasters and human induced factors. Still analysis accuracy issues play a vital role for the formulation of any strategy. To achieve better results, object based analysis methods have been tested. Various algorithms are developed by the analysts to calculate the magnitude of land cover changes. However, they must be evaluated for each environment that is under observation as mountainous areas. Here were object-based methods evaluated in comparison with pixel based. Landslides, soil moisture, soil permeability, snow cover and vegetation cover can be effectively monitored by those methods.展开更多
健康-安全-环境(health-safety-environment,HSE)绩效评价作为一种提升电力企业综合管理水平的关键手段,能够有效降低工程事故的发生概率且有力保障电力建设的顺利实施。然而,现有方法忽略各要素的可信度,导致HSE绩效评价结果的分辨率...健康-安全-环境(health-safety-environment,HSE)绩效评价作为一种提升电力企业综合管理水平的关键手段,能够有效降低工程事故的发生概率且有力保障电力建设的顺利实施。然而,现有方法忽略各要素的可信度,导致HSE绩效评价结果的分辨率较低。为此,提出一种基于模糊Petri网-长短期记忆网络(fuzzy Petri net-long short-term memory,FPN-LSTM)的电力建设项目HSE绩效评价方法。首先,根据现场HSE数据统计表引入模糊Petri网(fuzzy Petri net,FPN),构建直观化、定量化的电力建设项目HSE绩效评价模型;然后,计算库所节点数据的差异分数,同时采用线性插值的方式确定FPN模型中初始库所、中间库所和终止库所的置信度取值,开展电力建设项目HSE绩效评价;最后,借助长短期记忆网络(long short-term memory,LSTM)对FPN模型的置信度进行训练和更新,最大程度优化HSE绩效评价结果。以鲁西分公司的HSE绩效评价为例,开展FPN-LSTM模型验证,并将其与传统FPN模型、FPN-反向传播(back propagation,BP)神经网络进行对比分析,结果表明,该FPN-LSTM模型不仅能够准确、系统地反映整个电力建设项目的HSE绩效水平,还可精细、有效地厘清各级评价要素的执行情况和分布特性,从而为HSE管理人员提供科学化、体系化和精准化的决策依据。展开更多
During the process of organizing our original data,we unfortunately identified two error in the figures within our published article.In Fig.1,the online version incorrectly labels the SNI+NAC group as the sham+NAC gro...During the process of organizing our original data,we unfortunately identified two error in the figures within our published article.In Fig.1,the online version incorrectly labels the SNI+NAC group as the sham+NAC group.We have revised the grouping annotations in Fig.1 and have labeled the DHE staining in the figure to present the experimental design more clearly.展开更多
基金The National Natural Science Foundation of China under contract No.41201328the Science Foundation for Young Scholars of China’s State Oceanic Administration under contract No.2013415
文摘Mapping regional spatial patterns of coral reef geomorphology provides the primary information to understand the constructive processes in the reef ecosystem. However, this work is challenged by the pixel-based image classification method for its comparatively low accuracy. In this paper, an object-based image analysis(OBIA)method was presented to map intra-reef geomorphology of coral reefs in the Xisha Islands, China using Landsat 8satellite imagery. Following the work of the Millennium Coral Reef Mapping Project, a regional reef class hierarchy with ten geomorphic classes was first defined. Then, incorporating the hierarchical concept and integrating the spectral and additional spatial information such as context, shape and contextual relationships, a large-scale geomorphic map was produced by OBIA with accuracies generally more than 80%. Although the robustness of OBIA has been validated in the applications of coral reef mapping from individual reefs to reef system in this paper, further work is still required to improve its transferability.
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 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.
基金Under the auspices of Priority Academic Program Development of Jiangsu Higher Education Institutions,National Natural Science Foundation of China(No.41271438,41471316,41401440,41671389)
文摘Gully feature mapping is an indispensable prerequisite for the motioning and control of gully erosion which is a widespread natural hazard. The increasing availability of high-resolution Digital Elevation Model(DEM) and remote sensing imagery, combined with developed object-based methods enables automatic gully feature mapping. But still few studies have specifically focused on gully feature mapping on different scales. In this study, an object-based approach to two-level gully feature mapping, including gully-affected areas and bank gullies, was developed and tested on 1-m DEM and Worldview-3 imagery of a catchment in the Chinese Loess Plateau. The methodology includes a sequence of data preparation, image segmentation, metric calculation, and random forest based classification. The results of the two-level mapping were based on a random forest model after investigating the effects of feature selection and class-imbalance problem. Results show that the segmentation strategy adopted in this paper which considers the topographic information and optimal parameter combination can improve the segmentation results. The distribution of the gully-affected area is closely related to topographic information, however, the spectral features are more dominant for bank gully mapping. The highest overall accuracy of the gully-affected area mapping was 93.06% with four topographic features. The highest overall accuracy of bank gully mapping is 78.5% when all features are adopted. The proposed approach is a creditable option for hierarchical mapping of gully feature information, which is suitable for the application in hily Loess Plateau region.
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The detection of impervious surface (IS) in heterogeneous urban areas is one of the most challenging tasks in urban remote sensing. One of the limitations in IS detection at the parcel level is the lack of sufficient training data. In this study, a generic model of spatial distribution of roof materials is considered to overcome this limitation. A generic model that is based on spectral, spatial and textural information which is extracted from available training data is proposed. An object-based approach is used to extract the information inherent in the image. Furthermore, linear discriminant analysis is used for dimensionality reduction and to discriminate between different spatial, spectral and textural attributes. The generic model is composed of a discriminant function based on linear combinations of the predictor variables that provide the best discrimination among the groups. The discriminate analysis result shows that of the 54 attributes extracted from the WorldView-2 image, only 13 attributes related to spatial, spectral and textural information are useful for discriminating different roof materials. Finally, this model is applied to different WorldView-2 images from different areas and proves that this model has good potential to predict roof materials from the WorldView-2 images without using training data.
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.
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 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As forest is of great significance for our whole development and the sustainable plan is so focus on it. It is very urgent for us to have the whole distribution,stock volume and other related information about that. So the forest inventory program is on our schedule. Aiming at dealing with the problem in extraction of dominant tree species,we tested the highly hot method-object-based analysis. Based on the ALOS image data,we combined multi-resolution in e Cognition software and fuzzy classification algorithm. Through analyzing the segmentation results,we basically extract the spruce,the pine,the birch and the oak of the study area. Both the spectral and spatial characteristics were derived from those objects,and with the help of GLCM,we got the differences of each species. We use confusion matrix to do the Classification accuracy assessment compared with the actual ground data and this method showed a comparatively good precision as 87% with the kappa coefficient 0. 837.
文摘The majority of the population and economic activity of the northern half of Vietnam is clustered in the Red River Delta and about half of the country’s rice production takes place here. There are significant problems associated with its geographical position and the intensive exploitation of resources by an overabundant population (population density of 962 inhabitants/km2). Some thirty years after the economic liberalization and the opening of the country to international markets, agricultural land use patterns in the Red River Delta, particularly in the coastal area, have undergone many changes. Remote sensing is a particularly powerful tool in processing and providing spatial information for monitoring land use changes. The main methodological objective is to find a solution to process the many heterogeneous coastal land use parameters, so as to describe it in all its complexity, specifically by making use of the latest European satellite data (Sentinel-2). This complexity is due to local variations in ecological conditions, but also to anthropogenic factors that directly and indirectly influence land use dynamics. The methodological objective was to develop a new Geographic Object-based Image Analysis (GEOBIA) approach for mapping coastal areas using Sentinel-2 data and Landsat 8. By developing a new segmentation, accuracy measure, in this study was determined that segmentation accuracies decrease with increasing segmentation scales and that the negative impact of under-segmentation errors significantly increases at a large scale. An Estimation of Scale Parameter (ESP) tool was then used to determine the optimal segmentation parameter values. A popular machine learning algorithms (Random Forests-RFs) is used. For all classifications algorithm, an increase in overall accuracy was observed with the full synergistic combination of available data sets.
基金Supported by the National High Technology Research and Development Program of China(863 Program,2015AA016306)the National Natural Science Foundation of China(61662010,61231015,61471271)+1 种基金Science and Technology Plan Projects of Shenzhen(ZDSYS2014050916575763)Science and Technology Foundation of Guizhou Province(LKS[2011]1)
文摘This paper proposes an unequal error protection（UEP）coding method to improve the transmission performance of three-dimensional（3D）audio based on expanding window fountain（EWF）.Different from other transmissions with equal error protection（EEP）when transmitting the 3D audio objects.An approach of extracting the important audio object is presented,and more protection is given to more important audio object and comparatively less protection is given to the normal audio objects.Objective and subjective experiments have shown that the proposed UEP method achieves better performance than equal error protection method,while the bits error rates（BER）of the important audio object can decrease from 10^（–3） to 10^（–4）,and the subjective quality of UEP is better than that of EEP by 14%.
文摘Changing climate has a great impact on northern area of Pakistan＇s environment and is more prone to environmental changes impacts than rest of the country due to its high elevation. However, melting glaciers effect not only the local environment but also the whole country with frequent and heavy floods. Remote sensing （RS） from Satellites and Airplanes used in Geographical Information Systems （GIS） are technologies that can aid in understanding the on-going environmental processes. Furthermore, help researchers to observe, understand, forecast and suggest response to changes that occur. It can be natural disasters or man-made disasters and human induced factors. Still analysis accuracy issues play a vital role for the formulation of any strategy. To achieve better results, object based analysis methods have been tested. Various algorithms are developed by the analysts to calculate the magnitude of land cover changes. However, they must be evaluated for each environment that is under observation as mountainous areas. Here were object-based methods evaluated in comparison with pixel based. Landslides, soil moisture, soil permeability, snow cover and vegetation cover can be effectively monitored by those methods.
文摘健康-安全-环境(health-safety-environment,HSE)绩效评价作为一种提升电力企业综合管理水平的关键手段,能够有效降低工程事故的发生概率且有力保障电力建设的顺利实施。然而,现有方法忽略各要素的可信度,导致HSE绩效评价结果的分辨率较低。为此,提出一种基于模糊Petri网-长短期记忆网络(fuzzy Petri net-long short-term memory,FPN-LSTM)的电力建设项目HSE绩效评价方法。首先,根据现场HSE数据统计表引入模糊Petri网(fuzzy Petri net,FPN),构建直观化、定量化的电力建设项目HSE绩效评价模型;然后,计算库所节点数据的差异分数,同时采用线性插值的方式确定FPN模型中初始库所、中间库所和终止库所的置信度取值,开展电力建设项目HSE绩效评价;最后,借助长短期记忆网络(long short-term memory,LSTM)对FPN模型的置信度进行训练和更新,最大程度优化HSE绩效评价结果。以鲁西分公司的HSE绩效评价为例,开展FPN-LSTM模型验证,并将其与传统FPN模型、FPN-反向传播(back propagation,BP)神经网络进行对比分析,结果表明,该FPN-LSTM模型不仅能够准确、系统地反映整个电力建设项目的HSE绩效水平,还可精细、有效地厘清各级评价要素的执行情况和分布特性,从而为HSE管理人员提供科学化、体系化和精准化的决策依据。
文摘During the process of organizing our original data,we unfortunately identified two error in the figures within our published article.In Fig.1,the online version incorrectly labels the SNI+NAC group as the sham+NAC group.We have revised the grouping annotations in Fig.1 and have labeled the DHE staining in the figure to present the experimental design more clearly.
