Supervised classification of hyperspectral images via heterogeneous deep neural networks

Abstract

In this paper, a new heterogeneous neural networks based deep learning method, named HNNDL, is presented for supervised classification of hyperspectral image (HSI) with a small number of labeled samples. Specifically, a deep neural Network (DNN) and a convolutional neural network (CNN) are combined to build a HNNDL architecture. The proposed architecture contains three modules: 1) dimension reduction and feature extraction, 2) training pixel-wise DNN and CNN, 3) bilateral filtering based decision level fusion on two soft probability maps which is produced by above classifiers. The rationale behind this heterogeneous deep learning architecture is their ability to learn more abstract and robust local spectral-spatial information by taking full advantages of complementary ability of each networks, and thus boost the performance of HSI classifier. Experimental results on the widely used HSI indicate that the proposed approach outperforms several well-known classification methods in terms of classification accuracy.