Hyperspectral classification based on Siamese neural network using spectral-spatial feature

Recently, the deep convolutional neural network (CNN) is of great interest in hyperspectral image classification. However, limited available training samples still prevent CNN from exploring the performance of classification. In this work, we employ a novel pixel-pair method based on Siamese neural network (SNN) to significantly enlarge the training set and better represent the spectral-spatial features. In training, two pixels are respectively fed into two branch CNNs to extract deep features, where the same weights and biases are shared. Then, the absolute difference between the two deep features is learned by linear full connection layers with a given label. In testing, pixel-pairs, constructed by combining the center pixel and each of the surrounding pixels, are classified by the trained SNN. The final prediction is then determined by a voting strategy. The proposed SNN framework is extended to learn deep patch-pixel features. Experimental performance demonstrates that the proposed strategy outperforms the traditional classifiers, such as support vector machine (SVM) and extreme learning machine (ELM).