Unbalanced episode meta-learning with Bi-Sparse contrastive network for hyperspectral target detection

Abstract

Deep learning (DL) has been extensively applied to hyperspectral image target detection (HTD) with notable success. However, many existing DL-based methods focus on expanding the training samples to capture richer information, resulting in high computational costs and overfitting risks. Additionally, challenges such as complex data distributions and limited model transferability remain significant obstacles. To address these issues, we propose an unbalanced episode meta-learning with Bi-sparse contrastive network (UEML) for HTD. In contrast to directly modeling the target dataset, our approach leverages meta-learning to pre-train the model on a categorical dataset rich in label information, resulting in a universal detection model. Specifically, an unbalanced episode training paradigm is proposed for meta-task construction, which simulates the category-imbalance scenarios inherent to HTD by adaptively adjusting the support set, enabling the acquisition of content-agnostic yet task-relevant transferable meta-knowledge. Additionally, elastic sparsity constraints are imposed on the feature extraction process across both spatial and spectral dimensions, enhancing the model’s generalization and discriminative capabilities. During the fine-tuning phase, we employ a pseudo-sample generation strategy based on segmented sampling and spatial–spectral hybrid augmentation to construct the training set, allowing for more accurate and comprehensive sample extraction from complex background regions. This strategy effectively mitigates underfitting caused by insufficient information. Furthermore, contrastive learning is incorporated to address complexities arising by multi-class background characteristics in the pseudo-binary classification task, improving the stability of the detection model. Our proposed algorithm demonstrates rapid target detection capabilities, and experiments on six public datasets indicate that it performs significantly better than existing state-of-the-art methods. Code is available at: https://github.com/QYo-Liu/UEML.