Supervised Polarimetric SAR Image Classification Using Tensor Local Discriminant Embedding
文献类型：期刊论文
| 作者 | Huang, Xiayuan1; Qiao, Hong1,2,3; Zhang, Bo4,5 ; Nie, Xiangli1
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| 刊名 | IEEE TRANSACTIONS ON IMAGE PROCESSING
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| 出版日期 | 2018-06-01 |
| 卷号 | 27期号:6页码:2966-2979 |
| 关键词 | Land cover classification dimensionality reduction feature extraction spatial information polarimetric signature tensor local discriminant embedding PloSAR image |
| ISSN号 | 1057-7149 |
| DOI | 10.1109/TIP.2018.2815759 |
| 英文摘要 | 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. |
| 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 |
| 语种 | 英语 |
| WOS记录号 | WOS:000428930600006 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 源URL | [http://ir.amss.ac.cn/handle/2S8OKBNM/30145]  |
| 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. |
入库方式： OAI收割
来源：数学与系统科学研究院
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