Hyperspectral Target Detection with Hierarchical Denoising Autoencoder and Subspace Projection

Yanzi Shi, Keyan Wang, Jiaojiao Li, Yunsong Li

Abstract: Target detection technique in hyperspectral imagery has been widely applied in various applications. However, its performance is severely limited by the useless interference contained in hyperspectral images (HSIs), mainly caused by the atmosphere, illumination, issues within the sensor itself, and some other factors. In this paper, we propose a hyperspectral target detector based on linear mixture model (LMM), which consists of three components. First, a hierarchical denoising autoencoder (HDAE) is specifically designed for redundant interference removal; then we apply an adaptive cluster approach to extract several representative background samples from the clean HSI; lastly, a target detector with subspace projection is developed for background suppression and target enhancement based on the clean HSI, representative background and prior-known target signatures. Experimental results on two real-world HSIs show the superiority of our proposed method, namely, the HDASP detector, comparing with other state-of-the-art target detection methods.