Discriminative Feature Learning With Distance Constrained Stacked Sparse Autoencoder for Hyperspectral Target Detection

Abstract

Target detection (TD) is one of the major tasks in hyperspectral image (HSI) processing, and its performance is greatly affected by the background. Feature extraction (FE) has been an effective way to mine discriminative information, especially FE based on deep learning, which can learn the intrinsic properties of data to further improve the detection performance. Unlike supervised networks, unsupervised stacked sparse autoencoders (SSAEs) can learn deep and nonlinear features without any labeled data. However, SSAEs usually require a supervised fine-tuned model to obtain better discrimination, which is not feasible for TD, since the prior information is generally insufficient. In this letter, we introduce a distance constraint that is added to the SSAE to form a new distance constrained SSAE (DCSSAE) network. Specifically, the distance constraint maximizes the distinction between the target pixels and other background pixels in the feature space. Then, using the discriminative features learned from the DCSSAE, a simple detector using radial basis function kernel is derived for background suppression. Experiments on two HSIs demonstrate that the deep spectral features learned from the DCSSAE are more distinguishable, and our proposed detector, namely, the DCSSAE detector, outperforms several popular detectors, especially in background suppression.