提出了基于支持向量机(SVM)的单纯形增长算法(SGA)新实现方法, 该方法无需降维预处理, 且采用低复杂度的距离尺度代替复杂的体积尺度；证明了线性SVM与传统线性光谱混合模型(LSMM)在光谱解混中的等效性, 并探索了前者在信息的扩展利用和模型的非线性推广两方面的优势.实验结果表明, 基于SVM的SGA实现方法在保证选择结果不变的前提下复杂度大大降低,SVM模型下解混精度明显提高.

A new implementation method of simplex growing algorithm (SGA) is proposed based on support vector machine (SVM), which is free of dimensional reduction and makes use of distance measure instead of volume one. The unmixing equality of linear SVM and linear spectral mixing modeling (LSMM) is proved. The superiorities of linear SVM based spectral unmixing in two extended applications, combined use of endmember informations and nonlinearity use of the model, are explored. Experiments show that the computational complexity of the SVM based implementation method of SGA is decreased greatly, while the unmixing accuracy is obviously improved.