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Support Vector Machine-Based Endmember Extraction
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DOI：10.1109/TGRS.2008.2004708.png)
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摘要
En 中文
Introduced in this paper is the utilization of support vector machines (SVMs) to semiautomatically perform endmember extraction front hyperspectral data. The strengths of SVM are exploited to pro-vide a fast and accurate calculated representation of high-dimensional data sets that may consist of multiple distributions. Once this representation is computed, the number of distributions can be determined without prior knowledge. For each distribution, an optimal transform can be determined that preserves informational content while reducing the data dimensionality and, hence, the computational cost. Finally, endmember extraction for the whole data set is accomplished. Results indicate that this SVM-based endmember extraction algorithm has the capability of semiautonomously determining endmembers from multiple clusters with computational speed and accuracy while maintaining a robust tolerance to noise.
Keyword:
Endmember extraction
hyperspectral imaging
remote sensing
support vector machines (SVMs)
期刊
IF:
8.6
论文数:
2.2W
被引数:
10.7W
机构
引用论文
Applications of independent component analysis in endmember extraction and abundance quantification for hyperspectral imagery独立分量分析在高光谱图像的端元提取和丰度量化中的应用
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