Supervised Polarimetric SAR Image Classification Using Tensor Local Discriminant Embedding

doi:10.1109/TIP.2018.2815759

CSpace > 应用数学研究所

	Supervised Polarimetric SAR Image Classification Using Tensor Local Discriminant Embedding
	Huang, Xiayuan 1; Qiao, Hong 1,2,3; Zhang, Bo4,5 ; Nie, Xiangli 1
	2018-06-01
发表期刊	IEEE TRANSACTIONS ON IMAGE PROCESSING
ISSN	1057-7149
卷号	27 期号:6 页码:2966-2979
摘要	Feature 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.
关键词	Land cover classification dimensionality reduction feature extraction spatial information polarimetric signature tensor local discriminant embedding PloSAR image
DOI	10.1109/TIP.2018.2815759
语种	英语
资助项目	National 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研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS记录号	WOS:000428930600006
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计
文献类型	期刊论文
条目标识符	http://ir.amss.ac.cn/handle/2S8OKBNM/30146
专题	应用数学研究所
通讯作者	Zhang, Bo
作者单位	1.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
推荐引用方式 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.