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

作者：Zhao, Chunhui^*; Liu, Wu; Xu, Yan; Wen, Jinhuan

来源：Remote Sensing Letters, 2018, 9(3): 218-227.

DOI：10.1080/2150704X.2017.1415475

• MACHINES

Conventional 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.

• 出版日期2018
• 单位西北工业大学; 哈尔滨工程大学