An Abundance Characteristic-Based Independent Component Analysis for Hyperspectral Unmixing
文献类型：期刊论文
| 作者 | Wang, Nan1; Du, Bo1; Zhang, Liangpei1; Zhang, Lifu1 |
| 刊名 | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
 |
| 出版日期 | 2015 |
| 卷号 | 53期号:1 |
| 关键词 | Abundance characteristic convex geometry hyperspectral unmixing independent component analysis (ICA) orthogonal subspace projection |
| 通讯作者 | Wang, N (reprint author), Chinese Acad Sci, Inst Remote Sensing & Digital Earth RADI, State Key Lab Remote Sensing Sci, Beijing 100101, Peoples R China. |
| 英文摘要 | Independent component analysis (ICA) has been recently applied into hyperspectral unmixing as a result of its low computation time and its ability to perform without prior information. However, when applying ICA for hyperspectral unmixing, the independence assumption in the ICA model conflicts with the abundance sum-to-one constraint and the abundance nonnegative constraint in the linear mixture model, which affects the hyperspectral unmixing accuracy. In this paper, we consider an abundance matrix composed of Np-dimensional variables, and we propose a new hyperspectral unmixing approach with an abundance characteristic-based ICA model. Two characteristics of the abundance variables are explored, and the model is constructed by these characteristics. A corresponding gradient descent algorithm is also proposed to solve the proposed objective function. Both the synthetic and real experimental results demonstrate that the proposed method performs better than the other state-of-the-art methods in abundance and endmember extraction. |
| 研究领域[WOS] | Geochemistry & Geophysics ; Engineering, Electrical & Electronic ; Remote Sensing ; Imaging Science & Photographic Technology |
| 收录类别 | SCI ; EI |
| 语种 | 英语 |
| WOS记录号 | WOS:000341536700032 |
| 源URL | [http://ir.ceode.ac.cn/handle/183411/38349]  |
| 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.[Wang, Nan 2.Zhang, Lifu] Chinese Acad Sci, Inst Remote Sensing & Digital Earth RADI, State Key Lab Remote Sensing Sci, Beijing 100101, Peoples R China 3.[Du, Bo] Wuhan Univ, Sch Comp Sci, Wuhan 430072, Peoples R China 4.[Zhang, Liangpei] Wuhan Univ, Remote Sensing Grp, State Key Lab Informat Engn Surveying Mapping & R, Wuhan 430079, Peoples R China |
| 推荐引用方式 GB/T 7714 | Wang, Nan,Du, Bo,Zhang, Liangpei,et al. An Abundance Characteristic-Based Independent Component Analysis for Hyperspectral Unmixing[J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,2015,53(1). |
| APA | Wang, Nan,Du, Bo,Zhang, Liangpei,&Zhang, Lifu.(2015).An Abundance Characteristic-Based Independent Component Analysis for Hyperspectral Unmixing.IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,53(1). |
| MLA | Wang, Nan,et al."An Abundance Characteristic-Based Independent Component Analysis for Hyperspectral Unmixing".IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 53.1(2015). |
入库方式： OAI收割
来源：遥感与数字地球研究所
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