Spectral-spatial classification of hyperspectral images using deep Boltzmann machines

In the classification of hyperspectral images, extracting more expressive information on the ground objects from the data is a key problem in the classification method. For the purpose of improving classification accuracy, a classification method based on the Deep Boltzmann Machine (DBM) is proposed. First, PCA whitening is performed on the hyperspectral image data, and spatial information on pixels is extracted, followed by the combination with the spectral information on the pixel to construct hybrid spectral-spatial information on pixels; Second, deep features are extracted from the spectral-spatial information on pixels by the multi-layer DBM model; finally, the extracted features are classified based on the logistic regression model. The Deep Boltzmann Machine can extract features from high-dimensional data using prior knowledge, and the extracted features inherently represent the spatial structure and spectral characteristics of objects. Experimental results show that the proposed method can effectively improve the classification accuracy of hyperspectral images. © 2019, The Editorial Board of Journal of Xidian University. All right reserved.