Classification of Hyperspectral Images based on Intrinsic Image Decomposition and Deep Convolutional Neural Network

In this paper, a new simple spatial-spectral method is proposed to classify hyperspectral images. It is based on the combination of a deep convolutional neural network (DCNN) and intrinsic image decomposition (IID). First, the dimensionality of the hyperspectral image is reduced based on a band grouping technique and mean operator. Afterward, albedo and shading components of these reduced features are recovered. Finally, stacked albedo and shading components are classified by DCNN. Experiments are applied to Pavia University's hyperspectral image from an urban area. Classification accuracy of the proposed method with only I% of training data can reach about 99%, which is prominent according to state-of-the-art methods.