A spectral-spatial SVM-based multi-layer learning algorithm for hyperspectral image classification

Abstract

ABSTRACTConventional Markov random field (MRF) or Graph cut (GC) based support vector machine (SVM) methods for hyperspectral image (HSI) classification use MRF or GC to adjust spectral-based SVM results to increase the spatial consistency in an unsupervised way, thus, the pixels on the border and small-sized regions may be misclassified. In this letter, we propose a new framework of spectral-spatial SVM based multi-layer learning algorithm (SSMLL) for HSI classification. In the first layer of SSMLL, the spectral-based SVM is adopted to process the original HSI datasets; the nonlinear mapping is used to scale the first layer output and enhance the nonlinear structure in the second layer; in the last layer, the spatial information is incorporated into the SVM to obtain the final classification results in a supervised way. Experimental results show that the proposed SSMLL framework provides superior classification accuracy when compared to several state-of-the-art spectral-spatial SVM-based algorithms.