Region-Based Estimate of Endmember Variances for Hyperspectral Image Unmixing
文献类型：期刊论文
| 作者 | Gao, Lianru1; Zhuang, Lina1; Zhang, Bing1 |
| 刊名 | IEEE Geoscience and Remote Sensing Letters
 |
| 出版日期 | 2016 |
| 卷号 | 13期号:12页码:1807-1811 |
| 通讯作者 | Gao, Lianru (gaolr@radi.ac.cn) |
| 英文摘要 | Endmember variability is receiving growing attention in the hyperspectral image (HSI) unmixing field. As an extension of linear mixing model (LMM), normal compositional model (NCM) assumes that the pixels of the HSI are linear combinations of random endmembers (as opposed to deterministic for the LMM). NCM explains spectral differences between the observed pixels and endmembers as endmember mixtures and endmember variances, the characteristic of which makes it possible to incorporate the endmember spectral variability in the unmixing process. But the tricky issue for using NCM is the estimation of endmember variances inhering in materials. This letter presents a new approach, termed region-based stochastic expectation maximization, to learn endmember variances from spatial information. The idea is assuming that significant homogeneous regions (composed of similar materials or similar mixture) exist in the HSI, such regions usually give visual indication that spatial-based spectral variability really exists in hyperspectral data. As modeled in NCM, spectral variances in homogeneous region can be approximately linear represented by endmember variances. Hence, given region-based spectral variances, we are able to learn endmember variances. In experiments with simulated data and Moffett field data, the proposed approach competes with other unmixing methods considering endmember variability, with better endmember variance estimates. © 2004-2012 IEEE. |
| 收录类别 | EI |
| 语种 | 英语 |
| WOS记录号 | WOS:20165203171699 |
| 源URL | [http://ir.radi.ac.cn/handle/183411/39574]  |
| 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. Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 2.100094, China |
| 推荐引用方式 GB/T 7714 | Gao, Lianru,Zhuang, Lina,Zhang, Bing. Region-Based Estimate of Endmember Variances for Hyperspectral Image Unmixing[J]. IEEE Geoscience and Remote Sensing Letters,2016,13(12):1807-1811. |
| APA | Gao, Lianru,Zhuang, Lina,&Zhang, Bing.(2016).Region-Based Estimate of Endmember Variances for Hyperspectral Image Unmixing.IEEE Geoscience and Remote Sensing Letters,13(12),1807-1811. |
| MLA | Gao, Lianru,et al."Region-Based Estimate of Endmember Variances for Hyperspectral Image Unmixing".IEEE Geoscience and Remote Sensing Letters 13.12(2016):1807-1811. |
入库方式： OAI收割
来源：遥感与数字地球研究所
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