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It has been common to employ multiple features in the identification of the images acquired by hyperspectral remote sensing sensors, since more features give more information and have complementary properties. Few studies have discussed the combination strategies of multiple feature groups. This study made a systematic research on this problem. We extracted different groups of features from the initial hyperspectral images and tried different combination scenarios. We integrated spectral features with different textural features and employed different dimensionality reduction algorithms. Experimental results on three widely used hyperspectral remote sensing images suggested that “dimensionality reduction before combination” performed better especially when textural features performed well. The study further compared different combination frameworks of multiple feature groups, including direct combination, manifold learning, and multiple kernel method. The experimental results demonstrated the effectiveness of direct combination with an autoweight calculation.

A Research on the Combination Strategies of Multiple Features for Hyperspectral Remote Sensing Image Classification