Hyperspectral anomaly detection based on the distinguishing features of a redundant difference-value network

Abstract

ABSTRACT Hyperspectral anomaly detection is a key technique of unsupervised target detection. In the hyperspectral anomaly detection based on spectral dimensional transformation, the feature projection makes it easy to distinguish the ground objects which are not distinguishable in the original feature space. Although the means of spectral dimensional transformation can improve the distinguishable between diverse categories, it cannot highlight the anomalous targets. To be able to highlight anomalous targets while improving the diversity between different ground objects, an unsupervised network model of redundant difference-value network (RDVN) is proposed and applied to hyperspectral anomaly detection. RDVN is composed of multiple single-layer neural networks with the same structure and hyper-parameters. A group of training samples is used as the input of the networks, and the difference between the activation values of any network and benchmark network is used as the error for backpropagation. After the training is completed, the difference-value between the activation values of the two networks is used as a distinguishing feature (DF). Finally, DF is used as the input of the anomaly detector to obtain the detection results. Experimental results demonstrate that the proposed algorithm can achieve higher detection accuracy. DF not only highlights the anomalous target to increase the true positive rate but also increases the discriminability between different categories, thereby reducing the false-positive rate.