Hyperspectral image classification based on spectral-spatial feature extraction

A novel hyperspectral classification algorithm based on spectral-spatial feature extraction is proposed. First, spectral-spatial features are extracted by Gabor transform in PCA-projected space. Following that, Gabor-feature bands are partitioned into multiple subsets. Afterwards, the adjacent features in each subset are fused. Finally, the fused features are processed by recursive filtering before feeding into support vector machine (SVM) classifier. Experimental results demonstrate that the proposed algorithm substantially outperforms the traditional and state-of-the-art methods.