Contrastive Learning for Hyperspectral Target Detection

Abstract

With the development and progress of deep learning, the use of deep learning technology for hyperspectral target detection has achieved excellent results. However, most deep-learning-based methods do not effectively suppress background. This paper presents a contrastive learning-based hyperspectral target detection (CLHTD) for this purpose. The positive and negative pairs are constructed through data augmentation, and the backbone is used to extract the representative vectors of the augmented samples. Then the representative vectors are mapped to the spectral and the cluster contrast space using their corresponding contrastive head, respectively. In the contrast space, the similarity and dissimilarity of spectra and clusters are learned by maximizing the similarity of positive pairs while minimizing the similarity of negative pairs, to increase the difference between the representative vectors of target and background. Finally, the detection result is obtained through the cosine distance. Experimental results illustrate that the proposed CLHTD algorithm can achieve superior performances for hyperspectral target detection.