As the key technology of extracting remote sensing information,the classification of remote sensing images has always been the research focus in the field of remote sensing. The paper introduces the classification pro...As the key technology of extracting remote sensing information,the classification of remote sensing images has always been the research focus in the field of remote sensing. The paper introduces the classification process and system of remote sensing images. According to the recent research status of domestic and international remote sensing classification methods,the new study dynamics of remote sensing classification,such as artificial neural networks,support vector machine,active learning and ensemble multi-classifiers,were introduced,providing references for the automatic and intelligent development of remote sensing images classification.展开更多
Mudflat vegetation plays a crucial role in the ecological function of wetland environment,and obtaining its fine spatial distri-bution is of great significance for wetland protection and management.Remote sensing tech...Mudflat vegetation plays a crucial role in the ecological function of wetland environment,and obtaining its fine spatial distri-bution is of great significance for wetland protection and management.Remote sensing techniques can realize the rapid extraction of wetland vegetation over a large area.However,the imaging of optical sensors is easily restricted by weather conditions,and the backs-cattered information reflected by Synthetic Aperture Radar(SAR)images is easily disturbed by many factors.Although both data sources have been applied in wetland vegetation classification,there is a lack of comparative study on how the selection of data sources affects the classification effect.This study takes the vegetation of the tidal flat wetland in Chongming Island,Shanghai,China,in 2019,as the research subject.A total of 22 optical feature parameters and 11 SAR feature parameters were extracted from the optical data source(Sentinel-2)and SAR data source(Sentinel-1),respectively.The performance of optical and SAR data and their feature paramet-ers in wetland vegetation classification was quantitatively compared and analyzed by different feature combinations.Furthermore,by simulating the scenario of missing optical images,the impact of optical image missing on vegetation classification accuracy and the compensatory effect of integrating SAR data were revealed.Results show that:1)under the same classification algorithm,the Overall Accuracy(OA)of the combined use of optical and SAR images was the highest,reaching 95.50%.The OA of using only optical images was slightly lower,while using only SAR images yields the lowest accuracy,but still achieved 86.48%.2)Compared to using the spec-tral reflectance of optical data and the backscattering coefficient of SAR data directly,the constructed optical and SAR feature paramet-ers contributed to improving classification accuracy.The inclusion of optical(vegetation index,spatial texture,and phenology features)and SAR feature parameters(SAR index and SAR texture features)in the classification algorithm resulted in an OA improvement of 4.56%and 9.47%,respectively.SAR backscatter,SAR index,optical phenological features,and vegetation index were identified as the top-ranking important features.3)When the optical data were missing continuously for six months,the OA dropped to a minimum of 41.56%.However,when combined with SAR data,the OA could be improved to 71.62%.This indicates that the incorporation of SAR features can effectively compensate for the loss of accuracy caused by optical image missing,especially in regions with long-term cloud cover.展开更多
The classification of Chinese traditional settlements(CTSs)is extremely important for their differentiated development and protection.The innovative double-branch classification model developed in this study comprehen...The classification of Chinese traditional settlements(CTSs)is extremely important for their differentiated development and protection.The innovative double-branch classification model developed in this study comprehensively utilized the features of remote sensing(RS)images and building facade pictures(BFPs).This approach was able to overcome the limitations of previous methods that used only building facade images to classify settlements.First,the features of the roofs and walls were extracted using a double-branch structure,which consisted of an RS image branch and BFP branch.Then,a feature fusion module was designed to fuse the features of the roofs and walls.The precision,recall,and F1-score of the proposed model were improved by more than 4%compared with the classification model using only RS images or BFPs.The same three indexes of the proposed model were improved by more than 2%compared with other deep learning models.The results demonstrated that the proposed model performed well in the classification of architectural styles in CTSs.展开更多
Information on Land Use and Land Cover Map(LULCM)is essential for environment and socioeconomic applications.Such maps are generally derived from Multispectral Remote Sensing Images(MRSI)via classification.The classif...Information on Land Use and Land Cover Map(LULCM)is essential for environment and socioeconomic applications.Such maps are generally derived from Multispectral Remote Sensing Images(MRSI)via classification.The classification process can be described as information flow from images to maps through a trained classifier.Characterizing the information flow is essential for understanding the classification mechanism,providing solutions that address such theoretical issues as“what is the maximum number of classes that can be classified from a given MRSI?”and“how much information gain can be obtained?”Consequently,two interesting questions naturally arise,i.e.(i)How can we characterize the information flow?and(ii)What is the mathematical form of the information flow?To answer these two questions,this study first hypothesizes that thermodynamic entropy is the appropriate measure of information for both MRSI and LULCM.This hypothesis is then supported by kinetic-theory-based experiments.Thereafter,upon such an entropy,a generalized Jarzynski equation is formulated to mathematically model the information flow,which contains such parameters as thermodynamic entropy of MRSI,thermodynamic entropy of LULCM,weighted F1-score(classification accuracy),and total number of classes.This generalized Jarzynski equation has been successfully validated by hypothesis-driven experiments where 694 Sentinel-2 images are classified into 10 classes by four classical classifiers.This study provides a way for linking thermodynamic laws and concepts to the characterization and understanding of information flow in land cover classification,opening a new door for constructing domain knowledge.展开更多
Although the Convolutional Neural Network(CNN)has shown great potential for land cover classification,the frequently used single-scale convolution kernel limits the scope of informa-tion extraction.Therefore,we propos...Although the Convolutional Neural Network(CNN)has shown great potential for land cover classification,the frequently used single-scale convolution kernel limits the scope of informa-tion extraction.Therefore,we propose a Multi-Scale Fully Convolutional Network(MSFCN)with a multi-scale convolutional kernel as well as a Channel Attention Block(CAB)and a Global Pooling Module(GPM)in this paper to exploit discriminative representations from two-dimensional(2D)satellite images.Meanwhile,to explore the ability of the proposed MSFCN for spatio-temporal images,we expand our MSFCN to three-dimension using three-dimensional(3D)CNN,capable of harnessing each land cover category’s time series interac-tion from the reshaped spatio-temporal remote sensing images.To verify the effectiveness of the proposed MSFCN,we conduct experiments on two spatial datasets and two spatio-temporal datasets.The proposed MSFCN achieves 60.366%on the WHDLD dataset and 75.127%on the GID dataset in terms of mIoU index while the figures for two spatio-temporal datasets are 87.753%and 77.156%.Extensive comparative experiments and abla-tion studies demonstrate the effectiveness of the proposed MSFCN.展开更多
Considering the important applications in the military and the civilian domain, ship detection and classification based on optical remote sensing images raise considerable attention in the sea surface remote sensing f...Considering the important applications in the military and the civilian domain, ship detection and classification based on optical remote sensing images raise considerable attention in the sea surface remote sensing filed. This article collects the methods of ship detection and classification for practically testing in optical remote sensing images, and provides their corresponding feature extraction strategies and statistical data. Basic feature extraction strategies and algorithms are analyzed associated with their performance and application in ship detection and classification.Furthermore, publicly available datasets that can be applied as the benchmarks to verify the effectiveness and the objectiveness of ship detection and classification methods are summarized in this paper. Based on the analysis, the remaining problems and future development trends are provided for ship detection and classification methods based on optical remote sensing images.展开更多
Wetland research has become a hot spot linking multiple disciplines presently. Wetland classification and mapping is the basis for wetland research. It is difficult to generate wetland data sets using traditional meth...Wetland research has become a hot spot linking multiple disciplines presently. Wetland classification and mapping is the basis for wetland research. It is difficult to generate wetland data sets using traditional methods because of the low accessibility of wetlands, hence remote sensing data have become one of the primary data sources in wetland research. This paper presents a case study conducted at the core area of Honghe National Nature Reserve in the Sanjiang Plain, Northeast China. In this study, three images generated by airship, from Thematic Mapper and from SPOT 5 were selected to produce wetland maps at three different wetland landscape levels. After assessing classification accuracies of the three maps, we compared the different wetland mapping results of 11 plant communities to the airship image, 6 plant ecotypes to the TM image and 9 landscape classifications to the SPOT 5 image. We discussed the different characteristics of the hierarchical ecosystem classifications based on the spatial scales of the different images. The results indicate that spatial scales of remote sensing data have an important link to the hierarchies of wetland plant ecosystems displayed on the wetland landscape maps. The richness of wetland landscape information derived from an image closely relates to its spatial resolution. This study can enrich the ecological classification methods and mapping techniques dealing with the spatial scales of different remote sensing images. With a better understanding of classification accuracies in mapping wetlands by using different scales of remote sensing data, we can make an appropriate approach for dealing with the scale issue of remote sensing images.展开更多
How to recognize targets with similar appearances from remote sensing images(RSIs) effectively and efficiently has become a big challenge. Recently, convolutional neural network(CNN) is preferred in the target classif...How to recognize targets with similar appearances from remote sensing images(RSIs) effectively and efficiently has become a big challenge. Recently, convolutional neural network(CNN) is preferred in the target classification due to the powerful feature representation ability and better performance. However,the training and testing of CNN mainly rely on single machine.Single machine has its natural limitation and bottleneck in processing RSIs due to limited hardware resources and huge time consuming. Besides, overfitting is a challenge for the CNN model due to the unbalance between RSIs data and the model structure.When a model is complex or the training data is relatively small,overfitting occurs and leads to a poor predictive performance. To address these problems, a distributed CNN architecture for RSIs target classification is proposed, which dramatically increases the training speed of CNN and system scalability. It improves the storage ability and processing efficiency of RSIs. Furthermore,Bayesian regularization approach is utilized in order to initialize the weights of the CNN extractor, which increases the robustness and flexibility of the CNN model. It helps prevent the overfitting and avoid the local optima caused by limited RSI training images or the inappropriate CNN structure. In addition, considering the efficiency of the Na¨?ve Bayes classifier, a distributed Na¨?ve Bayes classifier is designed to reduce the training cost. Compared with other algorithms, the proposed system and method perform the best and increase the recognition accuracy. The results show that the distributed system framework and the proposed algorithms are suitable for RSIs target classification tasks.展开更多
Recently,the convolutional neural network(CNN)has been dom-inant in studies on interpreting remote sensing images(RSI).However,it appears that training optimization strategies have received less attention in relevant ...Recently,the convolutional neural network(CNN)has been dom-inant in studies on interpreting remote sensing images(RSI).However,it appears that training optimization strategies have received less attention in relevant research.To evaluate this problem,the author proposes a novel algo-rithm named the Fast Training CNN(FST-CNN).To verify the algorithm’s effectiveness,twenty methods,including six classic models and thirty archi-tectures from previous studies,are included in a performance comparison.The overall accuracy(OA)trained by the FST-CNN algorithm on the same model architecture and dataset is treated as an evaluation baseline.Results show that there is a maximal OA gap of 8.35%between the FST-CNN and those methods in the literature,which means a 10%margin in performance.Meanwhile,all those complex roadmaps,e.g.,deep feature fusion,model combination,model ensembles,and human feature engineering,are not as effective as expected.It reveals that there was systemic suboptimal perfor-mance in the previous studies.Most of the CNN-based methods proposed in the previous studies show a consistent mistake,which has made the model’s accuracy lower than its potential value.The most important reasons seem to be the inappropriate training strategy and the shift in data distribution introduced by data augmentation(DA).As a result,most of the performance evaluation was conducted based on an inaccurate,suboptimal,and unfair result.It has made most of the previous research findings questionable to some extent.However,all these confusing results also exactly demonstrate the effectiveness of FST-CNN.This novel algorithm is model-agnostic and can be employed on any image classification model to potentially boost performance.In addition,the results also show that a standardized training strategy is indeed very meaningful for the research tasks of the RSI-SC.展开更多
Hyperspectral remote sensing/imaging spectroscopy is a novel approach to reaching a spectrum from all the places of a huge array of spatial places so that several spectral wavelengths are utilized for making coherent ...Hyperspectral remote sensing/imaging spectroscopy is a novel approach to reaching a spectrum from all the places of a huge array of spatial places so that several spectral wavelengths are utilized for making coherent images.Hyperspectral remote sensing contains acquisition of digital images from several narrow,contiguous spectral bands throughout the visible,Thermal Infrared(TIR),Near Infrared(NIR),and Mid-Infrared(MIR)regions of the electromagnetic spectrum.In order to the application of agricultural regions,remote sensing approaches are studied and executed to their benefit of continuous and quantitativemonitoring.Particularly,hyperspectral images(HSI)are considered the precise for agriculture as they can offer chemical and physical data on vegetation.With this motivation,this article presents a novel Hurricane Optimization Algorithm with Deep Transfer Learning Driven Crop Classification(HOADTL-CC)model onHyperspectralRemote Sensing Images.The presentedHOADTL-CC model focuses on the identification and categorization of crops on hyperspectral remote sensing images.To accomplish this,the presentedHOADTL-CC model involves the design ofHOAwith capsule network(CapsNet)model for generating a set of useful feature vectors.Besides,Elman neural network(ENN)model is applied to allot proper class labels into the input HSI.Finally,glowworm swarm optimization(GSO)algorithm is exploited to fine tune the ENNparameters involved in this article.The experimental result scrutiny of the HOADTL-CC method can be tested with the help of benchmark dataset and the results are assessed under distinct aspects.Extensive comparative studies stated the enhanced performance of the HOADTL-CC model over recent approaches with maximum accuracy of 99.51%.展开更多
In order to improve the accuracy of building structure identification using remote sensing images,a building structure classification method based on multi-feature fusion of UAV remote sensing image is proposed in thi...In order to improve the accuracy of building structure identification using remote sensing images,a building structure classification method based on multi-feature fusion of UAV remote sensing image is proposed in this paper.Three identification approaches of remote sensing images are integrated in this method:object-oriented,texture feature,and digital elevation based on DSM and DEM.So RGB threshold classification method is used to classify the identification results.The accuracy of building structure classification based on each feature and the multi-feature fusion are compared and analyzed.The results show that the building structure classification method is feasible and can accurately identify the structures in large-area remote sensing images.展开更多
With the arrival of new data acquisition platforms derived from the Internet of Things(IoT),this paper goes beyond the understanding of traditional remote sensing technologies.Deep fusion of remote sensing and compute...With the arrival of new data acquisition platforms derived from the Internet of Things(IoT),this paper goes beyond the understanding of traditional remote sensing technologies.Deep fusion of remote sensing and computer vision has hit the industrial world and makes it possible to apply Artificial intelligence to solve problems such as automatic extraction of information and image interpretation.However,due to the complex architecture of IoT and the lack of a unified security protection mechanism,devices in remote sensing are vulnerable to privacy leaks when sharing data.It is necessary to design a security scheme suitable for computation‐limited devices in IoT,since traditional encryption methods are based on computational complexity.Visual Cryptography(VC)is a threshold scheme for images that can be decoded directly by the human visual system when superimposing encrypted images.The stacking‐to‐see feature and simple Boolean decryption operation make VC an ideal solution for privacy‐preserving recognition for large‐scale remote sensing images in IoT.In this study,the secure and efficient transmission of high‐resolution remote sensing images by meaningful VC is achieved.By diffusing the error between the encryption block and the original block to adjacent blocks,the degradation of quality in recovery images is mitigated.By fine‐tuning the pre‐trained model from large‐scale datasets,we improve the recognition performance of small encryption datasets for remote sensing images.The experimental results show that the proposed lightweight privacy‐preserving recognition framework maintains high recognition performance while enhancing security.展开更多
Purpose:Classification of remote sensing images(RSI)is a challenging task in computer vision.Recently,researchers have proposed a variety of creative methods for automatic recognition of RSI,and feature fusion is a re...Purpose:Classification of remote sensing images(RSI)is a challenging task in computer vision.Recently,researchers have proposed a variety of creative methods for automatic recognition of RSI,and feature fusion is a research hotspot for its great potential to boost performance.However,RSI has a unique imaging condition and cluttered scenes with complicated backgrounds.This larger difference from nature images has made the previous feature fusion methods present insignificant performance improvements.Design/methodology/approach:This work proposed a two-convolutional neural network(CNN)fusion method named main and branch CNN fusion network(MBC-Net)as an improved solution for classifying RSI.In detail,the MBC-Net employs an EfficientNet-B3 as its main CNN stream and an EfficientNet-B0 as a branch,named MC-B3 and BC-B0,respectively.In particular,MBC-Net includes a long-range derivation(LRD)module,which is specially designed to learn the dependence of different features.Meanwhile,MBC-Net also uses some unique ideas to tackle the problems coming from the two-CNN fusion and the inherent nature of RSI.Findings:Extensive experiments on three RSI sets prove that MBC-Net outperforms the other 38 state-of-theart(STOA)methods published from 2020 to 2023,with a noticeable increase in overall accuracy(OA)values.MBC-Net not only presents a 0.7%increased OA value on the most confusing NWPU set but also has 62%fewer parameters compared to the leading approach that ranks first in the literature.Originality/value:MBC-Net is a more effective and efficient feature fusion approach compared to other STOA methods in the literature.Given the visualizations of grad class activation mapping(Grad-CAM),it reveals that MBC-Net can learn the long-range dependence of features that a single CNN cannot.Based on the tendency stochastic neighbor embedding(t-SNE)results,it demonstrates that the feature representation of MBC-Net is more effective than other methods.In addition,the ablation tests indicate that MBC-Net is effective and efficient for fusing features from two CNNs.展开更多
In various fields,knowledge distillation(KD)techniques that combine vision transformers(ViTs)and convolutional neural networks(CNNs)as a hybrid teacher have shown remarkable results in classification.However,in the re...In various fields,knowledge distillation(KD)techniques that combine vision transformers(ViTs)and convolutional neural networks(CNNs)as a hybrid teacher have shown remarkable results in classification.However,in the realm of remote sensing images(RSIs),existing KD research studies are not only scarce but also lack competitiveness.This issue significantly impedes the deployment of the notable advantages of ViTs and CNNs.To tackle this,the authors introduce a novel hybrid‐model KD approach named HMKD‐Net,which comprises a CNN‐ViT ensemble teacher and a CNN student.Contrary to popular opinion,the authors posit that the sparsity in RSI data distribution limits the effectiveness and efficiency of hybrid‐model knowledge transfer.As a solution,a simple yet innovative method to handle variances during the KD phase is suggested,leading to substantial enhancements in the effectiveness and efficiency of hybrid knowledge transfer.The authors assessed the performance of HMKD‐Net on three RSI datasets.The findings indicate that HMKD‐Net significantly outperforms other cuttingedge methods while maintaining a significantly smaller size.Specifically,HMKD‐Net exceeds other KD‐based methods with a maximum accuracy improvement of 22.8%across various datasets.As ablation experiments indicated,HMKD‐Net has cut down on time expenses by about 80%in the KD process.This research study validates that the hybrid‐model KD technique can be more effective and efficient if the data distribution sparsity in RSIs is well handled.展开更多
An algorithm of hyperspectral remote sensing images classification is proposed based on the frequency spectrum of spectral signature.The spectral signature of each pixel in the hyperspectral image is taken as a discre...An algorithm of hyperspectral remote sensing images classification is proposed based on the frequency spectrum of spectral signature.The spectral signature of each pixel in the hyperspectral image is taken as a discrete signal,and the frequency spectrum is obtained using discrete Fourier transform.The discrepancy of frequency spectrum between ground objects' spectral signatures is visible,thus the difference between frequency spectra of reference and target spectral signature is used to measure the spectral similarity.Canberra distance is introduced to increase the contribution from higher frequency components.Then,the number of harmonics involved in the proposed algorithm is determined after analyzing the frequency spectrum energy cumulative distribution function of ground object.In order to evaluate the performance of the proposed algorithm,two hyperspectral remote sensing images are adopted as experimental data.The proposed algorithm is compared with spectral angle mapper (SAM),spectral information divergence (SID) and Euclidean distance (ED) using the product accuracy,user accuracy,overall accuracy,average accuracy and Kappa coefficient.The results show that the proposed algorithm can be applied to hyperspectral image classification effectively.展开更多
Hyperspectral remote sensing images terrain classification faces the problems of high data dimensionality and lack of labeled training data, resulting in unsatisfied terrain classification efficiency. The feature extr...Hyperspectral remote sensing images terrain classification faces the problems of high data dimensionality and lack of labeled training data, resulting in unsatisfied terrain classification efficiency. The feature extraction is required before terrain classification for preserving discriminative information and reducing data dimensionality. A hyperspectral remote sensing images feature extraction method, i.e., discrete cosine transform （DCT） spectral regression discriminant analysis （SRDA） subspace method, was presented to solve the above problems. The proposed DCT SRDA subspace method firstly takes DCT in the original spectral space and gets the DCT coefficients of each pixel spectral curve; secondly performs SRDA in the DCT coefficients space and obtains the DCT SRDA subspace. Minimum distance classifier was designed in the resulting DCT SRDA subspace to evaluate the feature extraction performance. Experiments for two real airborne visible/infrared imaging spectrometer （AVIRIS） hyperspectral images show that, comparing with spectral LDA subspace method, the proposed DCT SRDA subspace method can improve terrain classification efficiency.展开更多
The remote sensing ships’fine-grained classification technology makes it possible to identify certain ship types in remote sensing images,and it has broad application prospects in civil and military fields.However,th...The remote sensing ships’fine-grained classification technology makes it possible to identify certain ship types in remote sensing images,and it has broad application prospects in civil and military fields.However,the current model does not examine the properties of ship targets in remote sensing images with mixed multi-granularity features and a complicated backdrop.There is still an opportunity for future enhancement of the classification impact.To solve the challenges brought by the above characteristics,this paper proposes a Metaformer and Residual fusion network based on Visual Attention Network(VAN-MR)for fine-grained classification tasks.For the complex background of remote sensing images,the VAN-MR model adopts the parallel structure of large kernel attention and spatial attention to enhance the model’s feature extraction ability of interest targets and improve the classification performance of remote sensing ship targets.For the problem of multi-grained feature mixing in remote sensing images,the VAN-MR model uses a Metaformer structure and a parallel network of residual modules to extract ship features.The parallel network has different depths,considering both high-level and lowlevel semantic information.The model achieves better classification performance in remote sensing ship images with multi-granularity mixing.Finally,the model achieves 88.73%and 94.56%accuracy on the public fine-grained ship collection-23(FGSC-23)and FGSCR-42 datasets,respectively,while the parameter size is only 53.47 M,the floating point operations is 9.9 G.The experimental results show that the classification effect of VAN-MR is superior to that of traditional CNNs model and visual model with Transformer structure under the same parameter quantity.展开更多
Remote sensing image(RSI)classifier roles a vital play in earth observation technology utilizing Remote sensing(RS)data are extremely exploited from both military and civil fields.More recently,as novel DL approaches ...Remote sensing image(RSI)classifier roles a vital play in earth observation technology utilizing Remote sensing(RS)data are extremely exploited from both military and civil fields.More recently,as novel DL approaches develop,techniques for RSI classifiers with DL have attained important breakthroughs,providing a new opportunity for the research and development of RSI classifiers.This study introduces an Improved Slime Mould Optimization with a graph convolutional network for the hyperspectral remote sensing image classification(ISMOGCN-HRSC)model.The ISMOGCN-HRSC model majorly concentrates on identifying and classifying distinct kinds of RSIs.In the presented ISMOGCN-HRSC model,the synergic deep learning(SDL)model is exploited to produce feature vectors.The GCN model is utilized for image classification purposes to identify the proper class labels of the RSIs.The ISMO algorithm is used to enhance the classification efficiency of the GCN method,which is derived by integrating chaotic concepts into the SMO algorithm.The experimental assessment of the ISMOGCN-HRSC method is tested using a benchmark dataset.展开更多
As a significant role for traffic management, city planning, road monitoring, GPS navigation and map updating, the technology of road extraction from a remote sensing （RS） image has been a hot research topic in rece...As a significant role for traffic management, city planning, road monitoring, GPS navigation and map updating, the technology of road extraction from a remote sensing （RS） image has been a hot research topic in recent years. In this paper, after analyzing different road features and road models, the road extraction methods were classified into the classification-based methods, knowledge-based methods, mathematical morphology, active contour model, and dynamic programming. Firstly, the road features, road model, existing difficulties and interference factors for road extraction were analyzed. Secondly, the principle of road extraction, the advantages and disadvantages of various methods and research achievements were briefly highlighted. Then, the comparisons of the different road extraction algorithms were performed, including road features, test samples and shortcomings. Finally, the research results in recent years were summarized emphatically. It is obvious that only using one kind of road features is hard to get an excellent extraction effect. Hence, in order to get good results, the road extraction should combine multiple methods according to the real applications. In the future, how to realize the complete road extraction from a RS image is still an essential but challenging and important research topic.展开更多
The Sanjiang Plain, where nearly 20 kinds of wetlands exist now, is one of the largest wetlands distributed area of wetlands in China. To identify each of them and pick up them separately by means of automatic interpr...The Sanjiang Plain, where nearly 20 kinds of wetlands exist now, is one of the largest wetlands distributed area of wetlands in China. To identify each of them and pick up them separately by means of automatic interpretation of remote sensing from TM Landsat images is extremely important. However, most of the types of wetlands can not be divided each other due to the similarity and the illegibility of the wetland spectrum shown in TM images. Special disposals to remote sensing images include the spectrum enhancement of wetland information, the pseudo color composite of TM images of different bands and the algebra enhancement of TM images. By this way some kinds of wetlands such as Sparganium stoloniferum and Bolboschoenus maritimus can be identified. But in many cases, these methods are still insufficient because of the noise brought from the atmosphere transportation and so on. The physical features of wetlands reflecting the diversification of spectrum information of wetlands, which include the spatial temporal characteristics of the wetlands distribution, the landscape differences of wetlands from season to season, the growing environment and the vertical structure of wetlands vegetation and so on, must be taken into consideration. Besides these, the artificial alteration to spatial structure of wetlands such as the exploitation of some types of them can be also used as important symbols of wetlands identification from remote sensing images. On the basis of the above geographics analysis, a set of wetlands classification models of remote sensing could be established, and many types of wetlands such as paddy field, reed swamp, peat mire, meadow, CAREX marsh and paludification meadow and so on, will be distinguished consequently. All the ways of geographical analysis and model establishment will be given in detail in this article.展开更多
基金Supported by the Science Research Foundation(2010Y290) of Yunnan Department of Education
文摘As the key technology of extracting remote sensing information,the classification of remote sensing images has always been the research focus in the field of remote sensing. The paper introduces the classification process and system of remote sensing images. According to the recent research status of domestic and international remote sensing classification methods,the new study dynamics of remote sensing classification,such as artificial neural networks,support vector machine,active learning and ensemble multi-classifiers,were introduced,providing references for the automatic and intelligent development of remote sensing images classification.
基金Under the auspices of the National Key Research and Development Program of China(No.2023YFC3208500)Shanghai Municipal Natural Science Foundation(No.22ZR1421500)+3 种基金National Natural Science Foundation of China(No.U2243207)National Science and Technology Basic Resources Survey Project(No.2023FY01001)Open Research Fund of State Key Laboratory of Estuarine and Coastal Research(No.SKLEC-KF202406)Project from Science and Technology Commission of Shanghai Municipality(No.22DZ1202700)。
文摘Mudflat vegetation plays a crucial role in the ecological function of wetland environment,and obtaining its fine spatial distri-bution is of great significance for wetland protection and management.Remote sensing techniques can realize the rapid extraction of wetland vegetation over a large area.However,the imaging of optical sensors is easily restricted by weather conditions,and the backs-cattered information reflected by Synthetic Aperture Radar(SAR)images is easily disturbed by many factors.Although both data sources have been applied in wetland vegetation classification,there is a lack of comparative study on how the selection of data sources affects the classification effect.This study takes the vegetation of the tidal flat wetland in Chongming Island,Shanghai,China,in 2019,as the research subject.A total of 22 optical feature parameters and 11 SAR feature parameters were extracted from the optical data source(Sentinel-2)and SAR data source(Sentinel-1),respectively.The performance of optical and SAR data and their feature paramet-ers in wetland vegetation classification was quantitatively compared and analyzed by different feature combinations.Furthermore,by simulating the scenario of missing optical images,the impact of optical image missing on vegetation classification accuracy and the compensatory effect of integrating SAR data were revealed.Results show that:1)under the same classification algorithm,the Overall Accuracy(OA)of the combined use of optical and SAR images was the highest,reaching 95.50%.The OA of using only optical images was slightly lower,while using only SAR images yields the lowest accuracy,but still achieved 86.48%.2)Compared to using the spec-tral reflectance of optical data and the backscattering coefficient of SAR data directly,the constructed optical and SAR feature paramet-ers contributed to improving classification accuracy.The inclusion of optical(vegetation index,spatial texture,and phenology features)and SAR feature parameters(SAR index and SAR texture features)in the classification algorithm resulted in an OA improvement of 4.56%and 9.47%,respectively.SAR backscatter,SAR index,optical phenological features,and vegetation index were identified as the top-ranking important features.3)When the optical data were missing continuously for six months,the OA dropped to a minimum of 41.56%.However,when combined with SAR data,the OA could be improved to 71.62%.This indicates that the incorporation of SAR features can effectively compensate for the loss of accuracy caused by optical image missing,especially in regions with long-term cloud cover.
基金The Science and Technology Project of Hebei Education Department,No.BJK2022031The Open Fund of Hebei Key Laboratory of Geological Resources and Environmental Monitoring and Protection,No.JCYKT202310。
文摘The classification of Chinese traditional settlements(CTSs)is extremely important for their differentiated development and protection.The innovative double-branch classification model developed in this study comprehensively utilized the features of remote sensing(RS)images and building facade pictures(BFPs).This approach was able to overcome the limitations of previous methods that used only building facade images to classify settlements.First,the features of the roofs and walls were extracted using a double-branch structure,which consisted of an RS image branch and BFP branch.Then,a feature fusion module was designed to fuse the features of the roofs and walls.The precision,recall,and F1-score of the proposed model were improved by more than 4%compared with the classification model using only RS images or BFPs.The same three indexes of the proposed model were improved by more than 2%compared with other deep learning models.The results demonstrated that the proposed model performed well in the classification of architectural styles in CTSs.
基金supported by the National Natural Science Foundation of China[grant number 41930104]by the Research Grants Council of Hong Kong[grant number PolyU 152219/18E].
文摘Information on Land Use and Land Cover Map(LULCM)is essential for environment and socioeconomic applications.Such maps are generally derived from Multispectral Remote Sensing Images(MRSI)via classification.The classification process can be described as information flow from images to maps through a trained classifier.Characterizing the information flow is essential for understanding the classification mechanism,providing solutions that address such theoretical issues as“what is the maximum number of classes that can be classified from a given MRSI?”and“how much information gain can be obtained?”Consequently,two interesting questions naturally arise,i.e.(i)How can we characterize the information flow?and(ii)What is the mathematical form of the information flow?To answer these two questions,this study first hypothesizes that thermodynamic entropy is the appropriate measure of information for both MRSI and LULCM.This hypothesis is then supported by kinetic-theory-based experiments.Thereafter,upon such an entropy,a generalized Jarzynski equation is formulated to mathematically model the information flow,which contains such parameters as thermodynamic entropy of MRSI,thermodynamic entropy of LULCM,weighted F1-score(classification accuracy),and total number of classes.This generalized Jarzynski equation has been successfully validated by hypothesis-driven experiments where 694 Sentinel-2 images are classified into 10 classes by four classical classifiers.This study provides a way for linking thermodynamic laws and concepts to the characterization and understanding of information flow in land cover classification,opening a new door for constructing domain knowledge.
基金supported by the National Natural Science Foundation of China[grant number 41671452].
文摘Although the Convolutional Neural Network(CNN)has shown great potential for land cover classification,the frequently used single-scale convolution kernel limits the scope of informa-tion extraction.Therefore,we propose a Multi-Scale Fully Convolutional Network(MSFCN)with a multi-scale convolutional kernel as well as a Channel Attention Block(CAB)and a Global Pooling Module(GPM)in this paper to exploit discriminative representations from two-dimensional(2D)satellite images.Meanwhile,to explore the ability of the proposed MSFCN for spatio-temporal images,we expand our MSFCN to three-dimension using three-dimensional(3D)CNN,capable of harnessing each land cover category’s time series interac-tion from the reshaped spatio-temporal remote sensing images.To verify the effectiveness of the proposed MSFCN,we conduct experiments on two spatial datasets and two spatio-temporal datasets.The proposed MSFCN achieves 60.366%on the WHDLD dataset and 75.127%on the GID dataset in terms of mIoU index while the figures for two spatio-temporal datasets are 87.753%and 77.156%.Extensive comparative experiments and abla-tion studies demonstrate the effectiveness of the proposed MSFCN.
文摘Considering the important applications in the military and the civilian domain, ship detection and classification based on optical remote sensing images raise considerable attention in the sea surface remote sensing filed. This article collects the methods of ship detection and classification for practically testing in optical remote sensing images, and provides their corresponding feature extraction strategies and statistical data. Basic feature extraction strategies and algorithms are analyzed associated with their performance and application in ship detection and classification.Furthermore, publicly available datasets that can be applied as the benchmarks to verify the effectiveness and the objectiveness of ship detection and classification methods are summarized in this paper. Based on the analysis, the remaining problems and future development trends are provided for ship detection and classification methods based on optical remote sensing images.
基金Under the auspices of National Natural Science Foundation of China (No. 40871241, 40771170)National High Technology Research and Development Program of China (No. 2007AA12Z176)
文摘Wetland research has become a hot spot linking multiple disciplines presently. Wetland classification and mapping is the basis for wetland research. It is difficult to generate wetland data sets using traditional methods because of the low accessibility of wetlands, hence remote sensing data have become one of the primary data sources in wetland research. This paper presents a case study conducted at the core area of Honghe National Nature Reserve in the Sanjiang Plain, Northeast China. In this study, three images generated by airship, from Thematic Mapper and from SPOT 5 were selected to produce wetland maps at three different wetland landscape levels. After assessing classification accuracies of the three maps, we compared the different wetland mapping results of 11 plant communities to the airship image, 6 plant ecotypes to the TM image and 9 landscape classifications to the SPOT 5 image. We discussed the different characteristics of the hierarchical ecosystem classifications based on the spatial scales of the different images. The results indicate that spatial scales of remote sensing data have an important link to the hierarchies of wetland plant ecosystems displayed on the wetland landscape maps. The richness of wetland landscape information derived from an image closely relates to its spatial resolution. This study can enrich the ecological classification methods and mapping techniques dealing with the spatial scales of different remote sensing images. With a better understanding of classification accuracies in mapping wetlands by using different scales of remote sensing data, we can make an appropriate approach for dealing with the scale issue of remote sensing images.
基金supported by the National Natural Science Foundation of China(U1435220)
文摘How to recognize targets with similar appearances from remote sensing images(RSIs) effectively and efficiently has become a big challenge. Recently, convolutional neural network(CNN) is preferred in the target classification due to the powerful feature representation ability and better performance. However,the training and testing of CNN mainly rely on single machine.Single machine has its natural limitation and bottleneck in processing RSIs due to limited hardware resources and huge time consuming. Besides, overfitting is a challenge for the CNN model due to the unbalance between RSIs data and the model structure.When a model is complex or the training data is relatively small,overfitting occurs and leads to a poor predictive performance. To address these problems, a distributed CNN architecture for RSIs target classification is proposed, which dramatically increases the training speed of CNN and system scalability. It improves the storage ability and processing efficiency of RSIs. Furthermore,Bayesian regularization approach is utilized in order to initialize the weights of the CNN extractor, which increases the robustness and flexibility of the CNN model. It helps prevent the overfitting and avoid the local optima caused by limited RSI training images or the inappropriate CNN structure. In addition, considering the efficiency of the Na¨?ve Bayes classifier, a distributed Na¨?ve Bayes classifier is designed to reduce the training cost. Compared with other algorithms, the proposed system and method perform the best and increase the recognition accuracy. The results show that the distributed system framework and the proposed algorithms are suitable for RSIs target classification tasks.
基金Hunan University of Arts and Science provided doctoral research funding for this study (grant number 16BSQD23)Fund of Geography Subject ([2022]351)also provided funding.
文摘Recently,the convolutional neural network(CNN)has been dom-inant in studies on interpreting remote sensing images(RSI).However,it appears that training optimization strategies have received less attention in relevant research.To evaluate this problem,the author proposes a novel algo-rithm named the Fast Training CNN(FST-CNN).To verify the algorithm’s effectiveness,twenty methods,including six classic models and thirty archi-tectures from previous studies,are included in a performance comparison.The overall accuracy(OA)trained by the FST-CNN algorithm on the same model architecture and dataset is treated as an evaluation baseline.Results show that there is a maximal OA gap of 8.35%between the FST-CNN and those methods in the literature,which means a 10%margin in performance.Meanwhile,all those complex roadmaps,e.g.,deep feature fusion,model combination,model ensembles,and human feature engineering,are not as effective as expected.It reveals that there was systemic suboptimal perfor-mance in the previous studies.Most of the CNN-based methods proposed in the previous studies show a consistent mistake,which has made the model’s accuracy lower than its potential value.The most important reasons seem to be the inappropriate training strategy and the shift in data distribution introduced by data augmentation(DA).As a result,most of the performance evaluation was conducted based on an inaccurate,suboptimal,and unfair result.It has made most of the previous research findings questionable to some extent.However,all these confusing results also exactly demonstrate the effectiveness of FST-CNN.This novel algorithm is model-agnostic and can be employed on any image classification model to potentially boost performance.In addition,the results also show that a standardized training strategy is indeed very meaningful for the research tasks of the RSI-SC.
基金the Deanship of Scientific Research at King Khalid University for funding this work through Large Groups Project under Grant Number(25/43)Princess Nourah bint Abdulrahman University Researchers Supporting Project Number(PNURSP2022R303)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.The authors would like to thank the Deanship of Scientific Research at Umm Al-Qura University for supporting this work by Grant Code:22UQU4340237DSR28.
文摘Hyperspectral remote sensing/imaging spectroscopy is a novel approach to reaching a spectrum from all the places of a huge array of spatial places so that several spectral wavelengths are utilized for making coherent images.Hyperspectral remote sensing contains acquisition of digital images from several narrow,contiguous spectral bands throughout the visible,Thermal Infrared(TIR),Near Infrared(NIR),and Mid-Infrared(MIR)regions of the electromagnetic spectrum.In order to the application of agricultural regions,remote sensing approaches are studied and executed to their benefit of continuous and quantitativemonitoring.Particularly,hyperspectral images(HSI)are considered the precise for agriculture as they can offer chemical and physical data on vegetation.With this motivation,this article presents a novel Hurricane Optimization Algorithm with Deep Transfer Learning Driven Crop Classification(HOADTL-CC)model onHyperspectralRemote Sensing Images.The presentedHOADTL-CC model focuses on the identification and categorization of crops on hyperspectral remote sensing images.To accomplish this,the presentedHOADTL-CC model involves the design ofHOAwith capsule network(CapsNet)model for generating a set of useful feature vectors.Besides,Elman neural network(ENN)model is applied to allot proper class labels into the input HSI.Finally,glowworm swarm optimization(GSO)algorithm is exploited to fine tune the ENNparameters involved in this article.The experimental result scrutiny of the HOADTL-CC method can be tested with the help of benchmark dataset and the results are assessed under distinct aspects.Extensive comparative studies stated the enhanced performance of the HOADTL-CC model over recent approaches with maximum accuracy of 99.51%.
基金sponsored by National Key R&D Program of China(2018YFC1504504)Youth Foundation of Yunnan Earthquake Agency(2021K01)Project of Yunnan Earthquake Agency“Chuan bang dai”(CQ3-2021001).
文摘In order to improve the accuracy of building structure identification using remote sensing images,a building structure classification method based on multi-feature fusion of UAV remote sensing image is proposed in this paper.Three identification approaches of remote sensing images are integrated in this method:object-oriented,texture feature,and digital elevation based on DSM and DEM.So RGB threshold classification method is used to classify the identification results.The accuracy of building structure classification based on each feature and the multi-feature fusion are compared and analyzed.The results show that the building structure classification method is feasible and can accurately identify the structures in large-area remote sensing images.
基金supported in part by the National Natural Science Foundation of China under Grants(62250410365,62071084)the Guangdong Basic and Applied Basic Research Foundation of China(2022A1515011542)the Guangzhou Science and technology program of China(202201010606).
文摘With the arrival of new data acquisition platforms derived from the Internet of Things(IoT),this paper goes beyond the understanding of traditional remote sensing technologies.Deep fusion of remote sensing and computer vision has hit the industrial world and makes it possible to apply Artificial intelligence to solve problems such as automatic extraction of information and image interpretation.However,due to the complex architecture of IoT and the lack of a unified security protection mechanism,devices in remote sensing are vulnerable to privacy leaks when sharing data.It is necessary to design a security scheme suitable for computation‐limited devices in IoT,since traditional encryption methods are based on computational complexity.Visual Cryptography(VC)is a threshold scheme for images that can be decoded directly by the human visual system when superimposing encrypted images.The stacking‐to‐see feature and simple Boolean decryption operation make VC an ideal solution for privacy‐preserving recognition for large‐scale remote sensing images in IoT.In this study,the secure and efficient transmission of high‐resolution remote sensing images by meaningful VC is achieved.By diffusing the error between the encryption block and the original block to adjacent blocks,the degradation of quality in recovery images is mitigated.By fine‐tuning the pre‐trained model from large‐scale datasets,we improve the recognition performance of small encryption datasets for remote sensing images.The experimental results show that the proposed lightweight privacy‐preserving recognition framework maintains high recognition performance while enhancing security.
基金funded by Hunan University of Arts and Science(No:16BSQD23).
文摘Purpose:Classification of remote sensing images(RSI)is a challenging task in computer vision.Recently,researchers have proposed a variety of creative methods for automatic recognition of RSI,and feature fusion is a research hotspot for its great potential to boost performance.However,RSI has a unique imaging condition and cluttered scenes with complicated backgrounds.This larger difference from nature images has made the previous feature fusion methods present insignificant performance improvements.Design/methodology/approach:This work proposed a two-convolutional neural network(CNN)fusion method named main and branch CNN fusion network(MBC-Net)as an improved solution for classifying RSI.In detail,the MBC-Net employs an EfficientNet-B3 as its main CNN stream and an EfficientNet-B0 as a branch,named MC-B3 and BC-B0,respectively.In particular,MBC-Net includes a long-range derivation(LRD)module,which is specially designed to learn the dependence of different features.Meanwhile,MBC-Net also uses some unique ideas to tackle the problems coming from the two-CNN fusion and the inherent nature of RSI.Findings:Extensive experiments on three RSI sets prove that MBC-Net outperforms the other 38 state-of-theart(STOA)methods published from 2020 to 2023,with a noticeable increase in overall accuracy(OA)values.MBC-Net not only presents a 0.7%increased OA value on the most confusing NWPU set but also has 62%fewer parameters compared to the leading approach that ranks first in the literature.Originality/value:MBC-Net is a more effective and efficient feature fusion approach compared to other STOA methods in the literature.Given the visualizations of grad class activation mapping(Grad-CAM),it reveals that MBC-Net can learn the long-range dependence of features that a single CNN cannot.Based on the tendency stochastic neighbor embedding(t-SNE)results,it demonstrates that the feature representation of MBC-Net is more effective than other methods.In addition,the ablation tests indicate that MBC-Net is effective and efficient for fusing features from two CNNs.
基金Hunan University of Arts and Science,Grant/Award Numbers:JGYB2302Geography Subject[2022]351。
文摘In various fields,knowledge distillation(KD)techniques that combine vision transformers(ViTs)and convolutional neural networks(CNNs)as a hybrid teacher have shown remarkable results in classification.However,in the realm of remote sensing images(RSIs),existing KD research studies are not only scarce but also lack competitiveness.This issue significantly impedes the deployment of the notable advantages of ViTs and CNNs.To tackle this,the authors introduce a novel hybrid‐model KD approach named HMKD‐Net,which comprises a CNN‐ViT ensemble teacher and a CNN student.Contrary to popular opinion,the authors posit that the sparsity in RSI data distribution limits the effectiveness and efficiency of hybrid‐model knowledge transfer.As a solution,a simple yet innovative method to handle variances during the KD phase is suggested,leading to substantial enhancements in the effectiveness and efficiency of hybrid knowledge transfer.The authors assessed the performance of HMKD‐Net on three RSI datasets.The findings indicate that HMKD‐Net significantly outperforms other cuttingedge methods while maintaining a significantly smaller size.Specifically,HMKD‐Net exceeds other KD‐based methods with a maximum accuracy improvement of 22.8%across various datasets.As ablation experiments indicated,HMKD‐Net has cut down on time expenses by about 80%in the KD process.This research study validates that the hybrid‐model KD technique can be more effective and efficient if the data distribution sparsity in RSIs is well handled.
基金supported by the National Basic Research Program of China ("973" Program) (Grant No. 2010CB950800)International S&T Cooperation Program of China (Grant No. 2010DFA21880)China Postdoctoral Science Foundation (Grant No. 2012M510053)
文摘An algorithm of hyperspectral remote sensing images classification is proposed based on the frequency spectrum of spectral signature.The spectral signature of each pixel in the hyperspectral image is taken as a discrete signal,and the frequency spectrum is obtained using discrete Fourier transform.The discrepancy of frequency spectrum between ground objects' spectral signatures is visible,thus the difference between frequency spectra of reference and target spectral signature is used to measure the spectral similarity.Canberra distance is introduced to increase the contribution from higher frequency components.Then,the number of harmonics involved in the proposed algorithm is determined after analyzing the frequency spectrum energy cumulative distribution function of ground object.In order to evaluate the performance of the proposed algorithm,two hyperspectral remote sensing images are adopted as experimental data.The proposed algorithm is compared with spectral angle mapper (SAM),spectral information divergence (SID) and Euclidean distance (ED) using the product accuracy,user accuracy,overall accuracy,average accuracy and Kappa coefficient.The results show that the proposed algorithm can be applied to hyperspectral image classification effectively.
基金supported in part by the National Natural Science Foundation of China (61003199)the Natural Science Foundation of Shaanxi Province of China (2014JQ5183, 2014JM8331)the Special Foundation for Natural Science of the Education Department of Shaanxi Province of China (2013JK1129, 2013JK1075)
文摘Hyperspectral remote sensing images terrain classification faces the problems of high data dimensionality and lack of labeled training data, resulting in unsatisfied terrain classification efficiency. The feature extraction is required before terrain classification for preserving discriminative information and reducing data dimensionality. A hyperspectral remote sensing images feature extraction method, i.e., discrete cosine transform （DCT） spectral regression discriminant analysis （SRDA） subspace method, was presented to solve the above problems. The proposed DCT SRDA subspace method firstly takes DCT in the original spectral space and gets the DCT coefficients of each pixel spectral curve; secondly performs SRDA in the DCT coefficients space and obtains the DCT SRDA subspace. Minimum distance classifier was designed in the resulting DCT SRDA subspace to evaluate the feature extraction performance. Experiments for two real airborne visible/infrared imaging spectrometer （AVIRIS） hyperspectral images show that, comparing with spectral LDA subspace method, the proposed DCT SRDA subspace method can improve terrain classification efficiency.
文摘The remote sensing ships’fine-grained classification technology makes it possible to identify certain ship types in remote sensing images,and it has broad application prospects in civil and military fields.However,the current model does not examine the properties of ship targets in remote sensing images with mixed multi-granularity features and a complicated backdrop.There is still an opportunity for future enhancement of the classification impact.To solve the challenges brought by the above characteristics,this paper proposes a Metaformer and Residual fusion network based on Visual Attention Network(VAN-MR)for fine-grained classification tasks.For the complex background of remote sensing images,the VAN-MR model adopts the parallel structure of large kernel attention and spatial attention to enhance the model’s feature extraction ability of interest targets and improve the classification performance of remote sensing ship targets.For the problem of multi-grained feature mixing in remote sensing images,the VAN-MR model uses a Metaformer structure and a parallel network of residual modules to extract ship features.The parallel network has different depths,considering both high-level and lowlevel semantic information.The model achieves better classification performance in remote sensing ship images with multi-granularity mixing.Finally,the model achieves 88.73%and 94.56%accuracy on the public fine-grained ship collection-23(FGSC-23)and FGSCR-42 datasets,respectively,while the parameter size is only 53.47 M,the floating point operations is 9.9 G.The experimental results show that the classification effect of VAN-MR is superior to that of traditional CNNs model and visual model with Transformer structure under the same parameter quantity.
文摘Remote sensing image(RSI)classifier roles a vital play in earth observation technology utilizing Remote sensing(RS)data are extremely exploited from both military and civil fields.More recently,as novel DL approaches develop,techniques for RSI classifiers with DL have attained important breakthroughs,providing a new opportunity for the research and development of RSI classifiers.This study introduces an Improved Slime Mould Optimization with a graph convolutional network for the hyperspectral remote sensing image classification(ISMOGCN-HRSC)model.The ISMOGCN-HRSC model majorly concentrates on identifying and classifying distinct kinds of RSIs.In the presented ISMOGCN-HRSC model,the synergic deep learning(SDL)model is exploited to produce feature vectors.The GCN model is utilized for image classification purposes to identify the proper class labels of the RSIs.The ISMO algorithm is used to enhance the classification efficiency of the GCN method,which is derived by integrating chaotic concepts into the SMO algorithm.The experimental assessment of the ISMOGCN-HRSC method is tested using a benchmark dataset.
基金financially supported by the Special Fund for Basic Scientific Research of Central Colleges(No.2013G2241019)Shaanxi Province Science and Technology Fund(No.2013KW03)Xi'an City Science and Technology Fund(No.CX1252(8))
文摘As a significant role for traffic management, city planning, road monitoring, GPS navigation and map updating, the technology of road extraction from a remote sensing （RS） image has been a hot research topic in recent years. In this paper, after analyzing different road features and road models, the road extraction methods were classified into the classification-based methods, knowledge-based methods, mathematical morphology, active contour model, and dynamic programming. Firstly, the road features, road model, existing difficulties and interference factors for road extraction were analyzed. Secondly, the principle of road extraction, the advantages and disadvantages of various methods and research achievements were briefly highlighted. Then, the comparisons of the different road extraction algorithms were performed, including road features, test samples and shortcomings. Finally, the research results in recent years were summarized emphatically. It is obvious that only using one kind of road features is hard to get an excellent extraction effect. Hence, in order to get good results, the road extraction should combine multiple methods according to the real applications. In the future, how to realize the complete road extraction from a RS image is still an essential but challenging and important research topic.
文摘The Sanjiang Plain, where nearly 20 kinds of wetlands exist now, is one of the largest wetlands distributed area of wetlands in China. To identify each of them and pick up them separately by means of automatic interpretation of remote sensing from TM Landsat images is extremely important. However, most of the types of wetlands can not be divided each other due to the similarity and the illegibility of the wetland spectrum shown in TM images. Special disposals to remote sensing images include the spectrum enhancement of wetland information, the pseudo color composite of TM images of different bands and the algebra enhancement of TM images. By this way some kinds of wetlands such as Sparganium stoloniferum and Bolboschoenus maritimus can be identified. But in many cases, these methods are still insufficient because of the noise brought from the atmosphere transportation and so on. The physical features of wetlands reflecting the diversification of spectrum information of wetlands, which include the spatial temporal characteristics of the wetlands distribution, the landscape differences of wetlands from season to season, the growing environment and the vertical structure of wetlands vegetation and so on, must be taken into consideration. Besides these, the artificial alteration to spatial structure of wetlands such as the exploitation of some types of them can be also used as important symbols of wetlands identification from remote sensing images. On the basis of the above geographics analysis, a set of wetlands classification models of remote sensing could be established, and many types of wetlands such as paddy field, reed swamp, peat mire, meadow, CAREX marsh and paludification meadow and so on, will be distinguished consequently. All the ways of geographical analysis and model establishment will be given in detail in this article.
