Hyperspectral image classification based on deep auto-encoder and hidden Markov random field

Abstract

Hyperspectral Image (HSI) classification is one of the most persistent issue in remote sensing field. Recently, deep learning has attracted attention in HSI Classification field due to its accuracy and stronger generalization. This paper proposes a new spectral-spatial HSI classification approach developed on the deep learning concept of stacked-auto-encoders (SAE) based deep feature extraction and hidden Markov random field based segmentation. Specifically, First the SAE model is implemented as a spectral information-based classifier to extract the deep spectral features. Second, spatial information is obtained by using effective Hidden Markov random field (HMRF) based segmentation technique. Finally, maximum voting based criteria is employed to merge the extracted spectral and spatial information, which results in the precise spectral-spatial HSI classification. The characterization of the HSI with spectral spatial features results into more comprehensive analysis of HSI and to a more accurate classification. In general, use of spectral information resulted from the SAE process and spatial information by means of HMRF based segmentation and merging of spectral and spatial information by means of maximum voting based criteria, has a significant effect on the accuracy of the HSI classification. Experiments on real diverse hyperspectral data sets with different contexts and resolutions acquired by AVIRIS and ROSIS sensors show the accuracy of the proposed method and confirms that results of the proposed classification approach are comparable to several recently proposed HSI classification techniques.