Classification of hyperspectral image based on deep belief networks

Generally, dimensionality reduction methods, such as Principle Component Analysis (PCA) and Negative Matrix Factorization (NMF), are always applied as the preprocessing part in hyperspectral image classification so as to classify the constituent elements of every pixel in the scene efficiently. The results, however, would suffer the loss of detailed information inevitably. In this paper, deep learning frameworks, restricted Boltzmann machine (RBM) model and its deep structure deep belief networks (DBN), are introduced in hyperspectral image processing as the feature extraction and classification approach. The experiments are conducted on an airborne hyperspectral image. Further in the experiments, spatial-spectral classification is also practiced. Meanwhile, SVM with and without some classical feature extraction methods adopting before classification are employed as comparison. The results show the superior performance of the proposed approach.