Hyperspectral target detection is a promising yet challenging endeavor in remote sensing image processing. Representation learning-based detectors have become one of the mainstream methods to address the task. However, these methods often suffer from weak separability between the target and the background, which results in inferior target detection performance. The reason relies on that their detection model cannot effectively distinguish the subtle differences between the target and the background. To tackle this issue, this paper proposes a new weighted discriminative collaborative competitive representation model (denoted as WDCCR) for hyperspectral target detection. In WDCCR, the separability between targets and backgrounds is enhanced by integrating discriminative, competitive, and weight constraints. Meanwhile, to obtain pure background pixels for the representation model, we investigate a new category-based pixel selection method with target orthogonal purification. The proposed WDCCR target detector is evaluated on six hyperspectral datasets. Experimental results demonstrate that WDCCR outperforms other advanced methods, achieving good detection performance in hyperspectral target detection. The code will be available at https://github.com/liurongwhm.

Deep learning-based hyperspectral target detection (HTD) methods often face the challenge of insufficient prior information and difficulty in distinguishing local and global spectral differences. To address these problems, we propose a self-supervised framework that leverages contrastive learning to reduce dependence on prior knowledge, called the Gated Dual-Path Network with Contrastive Learning (GDPNCL). In this work, we introduce a novel sample augmentation strategy for deep network training, in which each pixel in the scene is processed using a dual concentric window to generate positive and negative samples. In addition, a Gated Dual-Path Network (GDPN) is proposed to effectively extract and discriminate local and global information from the spectra. Moreover, to mitigate the issue of false negative samples within the same class and to enhance the contrast between negative samples, we design a Weight Information Noise contrastive estimation (WIN) loss. The loss leverages the relationship between samples to further help the model learn representations that effectively distinguish targets from diverse backgrounds. Finally, the trained encoder is subsequently employed to extract features from the prior spectrum and test pixels, and the cosine similarity between them serves as the detection metric. Comprehensive experiments on four challenging hyperspectral datasets demonstrate that the GDPNCL outperforms state-of-the-art methods, highlighting its effectiveness and robustness in HTD.