Locality-preserving sparse representation-based classification in hyperspectral imagery
文献类型：期刊论文
| 作者 | Gao, Lianru1; Yu, Haoyang1; Zhang, Bing1; Li, Qingting1 |
| 刊名 | Journal of Applied Remote Sensing
 |
| 出版日期 | 2016 |
| 卷号 | 10期号:4 |
| 关键词 | RADIOMETRIC SLOPE CORRECTION SAR IMAGERY LINE DETECTION TERRAIN SNOW BACKSCATTER PRODUCTS SRTM |
| 通讯作者 | Li, Qingting (liqt@radi.ac.cn) |
| 英文摘要 | This paper proposes to combine locality-preserving projections (LPP) and sparse representation (SR) for hyperspectral image classification. The LPP is first used to reduce the dimensionality of all the training and testing data by finding the optimal linear approximations to the eigenfunctions of the Laplace Beltrami operator on the manifold, where the high-dimensional data lies. Then, SR codes the projected testing pixels as sparse linear combinations of all the training samples to classify the testing pixels by evaluating which class leads to the minimum approximation error. The integration of LPP and SR represents an innovative contribution to the literature. The proposed approach, called locality-preserving SR-based classification, addresses the imbalance between high dimensionality of hyperspectral data and the limited number of training samples. Experimental results on three real hyperspectral data sets demonstrate that the proposed approach outperforms the original counterpart, i.e., SR-based classification. © 2016 Society of Photo-Optical Instrumentation Engineers (SPIE). |
| 学科主题 | Environmental Sciences & Ecology; Remote Sensing; Imaging Science & Photographic Technology |
| 类目[WOS] | Environmental Sciences ; Remote Sensing ; Imaging Science & Photographic Technology |
| 收录类别 | SCI ; EI |
| 语种 | 英语 |
| WOS记录号 | WOS:20162602538118 |
| 源URL | [http://ir.radi.ac.cn/handle/183411/39379]  |
| 专题 | 遥感与数字地球研究所_SCI/EI期刊论文_期刊论文 |
| 作者单位 | 1. Chinese Academy of Sciences, Institute of Remote Sensing and Digital Earth, Key Laboratory of Digital Earth Science, No. 9 Dengzhuang South Road, Beijing 2.100094, China 3. University of Chinese Academy of Sciences, No. 19A Yuquan Road, Beijing 4.100049, China |
| 推荐引用方式 GB/T 7714 | Gao, Lianru,Yu, Haoyang,Zhang, Bing,et al. Locality-preserving sparse representation-based classification in hyperspectral imagery[J]. Journal of Applied Remote Sensing,2016,10(4). |
| APA | Gao, Lianru,Yu, Haoyang,Zhang, Bing,&Li, Qingting.(2016).Locality-preserving sparse representation-based classification in hyperspectral imagery.Journal of Applied Remote Sensing,10(4). |
| MLA | Gao, Lianru,et al."Locality-preserving sparse representation-based classification in hyperspectral imagery".Journal of Applied Remote Sensing 10.4(2016). |
入库方式： OAI收割
来源：遥感与数字地球研究所
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