Hyperspectral image classification based on spectral-spatial feature extraction

Abstract

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.

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Cite this paper

@article{Ye2017HyperspectralIC, title={Hyperspectral image classification based on spectral-spatial feature extraction}, author={Zhen Ye and Lian Tan and Lin Bai}, journal={2017 International Workshop on Remote Sensing with Intelligent Processing (RSIP)}, year={2017}, pages={1-4} }