Deep feature fusion via two-stream convolutional neural network for hyperspectral image classification

The representation power of convolutional neural network (CNN) models for hyperspectral image (HSI) analysis is in practice limited by the available amount of the labeled samples, which is often insufficient to sustain deep networks with many parameters. We propose a novel approach to boost the network representation power with a two-stream 2-D CNN architecture. The proposed method extracts simultaneously, the spectral features and local spatial and global spatial features, with two 2-D CNN networks and makes use of channel correlations to identify the most informative features. Moreover, we propose a layer-specific regularization and a smooth normalization fusion scheme to adaptively learn the fusion weights for the spectral-spatial features from the two parallel streams. An important asset of our model is the simultaneous training of the feature extraction, fusion, and classification processes with the same cost function. Experimental results on several hyperspectral data sets demonstrate the efficacy of the proposed method compared with the state-of-the-art methods in the field

Hyperspectral image classification using joint sparse model and discontinuity preserving relaxation

© 2017 IEEE. As a promising signal processing technique, a joint sparse model (JSM) has been used to integrate spatial and spectral information in the classification of remotely sensed images. This technique defines a local region of a fixed window size and assumes an equal contribution from each neighborhood pixel in the classification process of the test pixel. However, equal weighting is less reasonable for heterogeneous pixels, especially around class boundaries. Hence, a discontinuity preserving relaxation (DPR) method can be used to locally smooth the results without crossing the boundaries by detecting the discontinuities of an image in advance. In this letter, we developed a novel strategy that combines these two methods to improve the hyperspectral image classification. A JSM is first applied to obtain a posteriori probability distribution of pixels and then a DPR method is used to further improve the classification results. Experiments conducted on two benchmark data sets demonstrate that the proposed method leads to superior performance when compared with several popular algorithms