SVM-Based Unmixing-to-Classification Conversion for Hyperspectral Abundance Quantification
SVM-Based Unmixing-to-Classification Conversion for Hyperspectral Abundance Quantification
复制标题
基于 SVM 的高光谱丰度量化的分解到分类转换
DOI：
10.1109/tgrs.2011.2166766
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发表时间：
2011-10
影响因子：
8.2
通讯作者：
Zhang, Ye
中科院分区：
文献类型：
--
作者：
Mianji, Fereidoun A.;Zhang, Ye
Need for a priori knowledge of the components comprising each pixel in a scene has set the endmember determination, rather than the endmember abundance quantification, as the primary focus of many unmixing approaches. In the absence of the information about the pure signatures present in an image scene, which is often the case, the mean spectra of the pixel vectors, directly extracted from the scene, are usually used as the pure signatures' spectra. This approach which is mathematically optimized for unmixing problems with a priori known information ignores some statistical properties of the extracted samples and leads to a suboptimal solution for real situations. This paper proposes a novel learning-based unmixing-to-classification conversion model to treat the abundance quantification task as a classification problem. Support vector machine, as an efficient classifier, is used to realize this model. It exploits the statistical nature (endmember spectral variability) of the extracted endmember representatives from the hyperspectral scene, rather than solving the problem according to the ideal model in which only the mean spectra of each training sample set is used. Several experiments are carried out on simulated and real hyperspectral images. The obtained results validate the high performance of the proposed technique in abundance quantification which is a key subpixel information detection capability.
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影响因子：
8.2
作者：
L. Jimenez;Jorge L. Rivera-Medina;Eladio Rodriguez-Diaz;E. Arzuaga-Cruz;Mabel Ramírez-Vélez
通讯作者：
L. Jimenez;Jorge L. Rivera-Medina;Eladio Rodriguez-Diaz;E. Arzuaga-Cruz;Mabel Ramírez-Vélez
DOI：
10.1109/icassp.2009.4959777
发表时间：
2009-04
期刊：
2009 IEEE International Conference on Acoustics, Speech and Signal Processing
影响因子：
--
作者：
Tsung-Han Chan;Chong-Yung Chi;Yu-Min Huang;Wing-Kin Ma
通讯作者：
Tsung-Han Chan;Chong-Yung Chi;Yu-Min Huang;Wing-Kin Ma
DOI：
--
发表时间：
2002-09
期刊：
--
影响因子：
--
作者：
A. Cichocki;S. Amari
通讯作者：
A. Cichocki;S. Amari
影响因子：
4.8
作者：
B. Demir;S. Ertürk
通讯作者：
B. Demir;S. Ertürk
DOI：
10.1007/978-3-540-92910-9_13
发表时间：
2012
期刊：
--
影响因子：
--
作者：
Seungjin Choi
通讯作者：
Seungjin Choi