关键词: 高光谱图像分类, 同质性降维, 稀疏最小二乘支持向量机, 组合匹配追踪算法

Abstract: According to the fact that high-dimensionality and redundant information between bands of hyperspectral data, the paper proposes a new classification method based on homogeneity dimensionality reduction and Combined Matching Pursuit（CMP） algorithm. Firstly, the proposed method segments hyperspectral image by mean shift algorithm and obtains homogeneous blocks, and builds dimensionality reduction mapping function of the image block by manifold learning. And then the method trains sparse least squares SVM for low-dimensional data, but the method proposes a  new sparse reconstruction method based on CMP algorithm for solving multiple iterations of Orthogonal Matching Pursuit（OMP） algorithm. By classification results of hyperspectral data can be drawn that the proposed method effectively improves the hyperspectral image classification accuracy.

Key words: hyperspectral image classification, homogeneity dimensionality reduction, sparse least squares SVM, Combined Matching Pursuit（CMP） algorithm