Hyperspectral classification via deep networks and superpixel segmentation

Abstract

This article presents a new hyperspectral image classification method, which is capable of automatic feature learning while achieving high classification accuracy. The method contains the following two major modules: the spectral classification module and the spatial constraints module. Spectral classification module uses a deep network, called ‘Stacked Denoising Autoencoders’ (SdA), to learn feature representation of the data. Through SdA, the data are projected non-linearly from its original hyperspectral space to some higher-dimensional space, where more compact distribution is obtained. An interesting aspect of this method is that it does not need any prior feature design/extraction process guided by human. The suitable feature for the classification is learnt by the deep network itself. Superpixel is utilized to generate the spatial constraints for the refinement of the spectral classification results. By exploiting the spatial consistency of neighbourhood pixels, the accuracy of classification is further improved by a big margin. Experiments on the public data sets have revealed the superior performance of the proposed method.