In this paper, we propose a novel residual fusion classification method for hyperspectral image using spatial–spectral information, abbreviated as RFC-SS. The RFC-SS method first uses the Gabor texture features and the non-parametric weighted spectral features to describe the
hyperspectral image from both aspects of spatial and spectral information. Then it applies the residual fusion method to save the useful information from different classification methods, which can greatly improve the classification performance. Finally, the test sample is assigned to the
class that has the minimal fused residuals. The RFC-SS classification method is tested on two classical hyperspectral images (i.e. Indian Pines, Pavia University). The theoretical analysis and experimental results demonstrate that the RFC-SS classification method can achieve a better performance
in terms of overall accuracy, average accuracy, and the Kappa coefficient when compared to the other classification methods.
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Document Type: Research Article
College of Information and Communication Engineering, Harbin Engineering University, Harbin, China
Institute of Telecommunication Satellites, China Academy of Space Technology, Beijing, China
Publication date: February 16, 2016
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