Robust Signature-Based Hyperspectral Target Detection Using Dual Networks

Abstract

The training of deep networks for hyperspectral target detection (HTD) is usually confronted with the problem of limited samples and in extreme cases, there might be only one target sample available. To address this challenge, we propose a novel approach with dual networks in this letter. First, a training set that is not fully accurate but representative enough regarding both targets and backgrounds is built through predetection and clustering. Then, two types of neural networks, that is, one generative adversarial network (GAN) and one convolutional neural network (CNN), which focus on spectral and spatial features of hyperspectral images (HSIs), are utilized for target detection. After that, the results of the two networks are fused, with the final detection result obtained. Experiments on real HSIs indicate that the proposed approach manages to perform HTD with only one target sample and is able to yield a more robust detection performance compared to other approaches.