FUSING DIFFERENT LEVELS OF DEEP FEATURES BY DEEP STACKED NEURAL NETWORK FOR HYPERSPECTRAL IMAGES

Deep learning techniques have been demonstrated to be a powerful tool to learn features of images automatically. In this paper, a novel deep learning structure, i.e., deep stacked neural network (DSNN), is constructed to extract different levels of deep features of hyperspectral images. Specifically, convolutional neural network (CNN) is used as basic units in the proposed DSNN for feature extraction of hyperspectral images. Then, different levels of deep features are concatenated to form a novel fused feature for classification with a typical classifier, e.g., SVM. Experimental results on two benchmark hyperspectral datasets show that the fusion of features extracted in DSNN can produce higher classification accuracy than state-of-the-art deep learning based methods, indicating its effectiveness in feature learning.