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Supervised Polarimetric SAR Image Classification Using Tensor Local Discriminant Embedding
Huang, Xiayuan1; Qiao, Hong1,2,3; Zhang, Bo4,5; Nie, Xiangli1
2018-06-01
Source PublicationIEEE TRANSACTIONS ON IMAGE PROCESSING
ISSN1057-7149
Volume27Issue:6Pages:2966-2979
AbstractFeature extraction is a very important step for polarimetric synthetic aperture radar (PolSAR) image classification. Many dimensionality reduction (DR) methods have been employed to extract features for supervised PolSAR image classification. However, these DR-based feature extraction methods only consider each single pixel independently and thus fail to take into account the spatial relationship of the neighboring pixels, so their performance may not be satisfactory. To address this issue, we introduce a novel tensor local discriminant embedding (TLDE) method for feature extraction for supervised PolSAR image classification. The proposed method combines the spatial and polarimetric information of each pixel by characterizing the pixel with the patch centered at this pixel. Then each pixel is represented as a third-order tensor of which the first two modes indicate the spatial information of the patch (i.e., the row and the column of the patch) and the third mode denotes the polarimetric information of the patch. Based on the label information of samples and the redundance of the spatial and polarimetric information, a supervised tensor-based DR technique, called TLDE, is introduced to find three projections which project each pixel, that is, the third-order tensor into the low-dimensional feature. Finally, classification is completed based on the extracted features using the nearest neighbor classifier and the support vector machine classifier. The proposed method is evaluated on two real PolSAR data sets and the simulated PolSAR data sets with various number of looks. The experimental results demonstrate that the proposed method not only improves the classification accuracy greatly but also alleviates the influence of speckle noise on classification.
KeywordLand cover classification dimensionality reduction feature extraction spatial information polarimetric signature tensor local discriminant embedding PloSAR image
DOI10.1109/TIP.2018.2815759
Language英语
Funding ProjectNational Natural Science Foundation of China[61602483] ; National Natural Science Foundation of China[61627808] ; National Natural Science Foundation of China[91648205] ; National Natural Science Foundation of China[61379093] ; China Postdoctoral Science Foundation[2017M620953] ; Strategic Priority Research Program of the CAS[XDB02080003] ; Beijing Natural Science Foundation[4174107]
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS IDWOS:000428930600006
PublisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Citation statistics
Cited Times:2[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.amss.ac.cn/handle/2S8OKBNM/30146
Collection应用数学研究所
Affiliation1.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
2.CAS Ctr Excellence Brain Sci & Intelligence Techn, Shanghai 200031, Peoples R China
3.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China
4.Chinese Acad Sci, Inst Appl Math, LSEC, AMSS, Beijing 100190, Peoples R China
5.Univ Chinese Acad Sci, Sch Math Sci, Beijing 100049, Peoples R China
Recommended Citation
GB/T 7714
Huang, Xiayuan,Qiao, Hong,Zhang, Bo,et al. Supervised Polarimetric SAR Image Classification Using Tensor Local Discriminant Embedding[J]. IEEE TRANSACTIONS ON IMAGE PROCESSING,2018,27(6):2966-2979.
APA Huang, Xiayuan,Qiao, Hong,Zhang, Bo,&Nie, Xiangli.(2018).Supervised Polarimetric SAR Image Classification Using Tensor Local Discriminant Embedding.IEEE TRANSACTIONS ON IMAGE PROCESSING,27(6),2966-2979.
MLA Huang, Xiayuan,et al."Supervised Polarimetric SAR Image Classification Using Tensor Local Discriminant Embedding".IEEE TRANSACTIONS ON IMAGE PROCESSING 27.6(2018):2966-2979.
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