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Adaptive Temperature-Driven Ternary Contrastive Autoencoder Framework for Hyperspectral Target Detection
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AI
DOI：10.1109/TGRS.2025.3630752.png)
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Abstract
En 中文
Hyperspectral target detection (HTD) is a critical task in remote sensing, where numerous deep learning (DL)-based methods have emerged for their powerful ability to extract hierarchical and discriminative features. However, challenges such as insufficient labeled samples and spectral variability lead to formidable issues for DL-based methods like model underfitting and poor robustness. In particular, existing contrastive learning (CL)-based detectors rely on native positive–negative pair construction while overlooking hidden positive pairs (i.e., positive but mistakenly constructed as negative), which undermines the model’s ability to maintain consistent feature representation. To address the above issues, we propose an adaptive temperature-driven ternary contrastive autoencoder (ATTCA) framework, which performs HTD in a self-supervised manner. Initially, we introduce a novel augmentation technique named strong–weak frequency-domain interference (S-WFI) to expand data while establishing the ternary framework, which can balance the robustness and representation consistency of the model. Additionally, a dual-stream quad-scan Mamba (DSQM) network based on a compositing selective state-space model (SSM) is tailored to effectively extract multiscale spatial–spectral features and mitigate the effects of spectral variation. Ultimately, we formulate a reconstruction weight-driven adaptive temperature (RWAT) strategy to dynamically adjust parameters and suppress the separation of hidden positive pairs, which can facilitate the alignment of target features effectively. Experimental results on four real-world benchmark datasets demonstrate that our approach outperforms state-of-the-art methods in terms of detection performance and efficiency.
Keywords:
Contrastive learning (CL)
hyperspectral target detection (HTD)
self-supervised learning
state-space model (SSM)
Journal
IF:
8.6
Papers:
2.1W
Citations:
10.7W
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