Regions of Interest Extraction for Hyperspectral Small Targets Based on Self-Supervised Learning

The extraction of regions of interest (ROI) plays a vital role in enhancing the precision of target analysis and identification, especially for hyperspectral small targets in wide fields of view. While the use of deep learning for ROI extraction has demonstrated significant potential, its efficacy has been hindered by a scarcity of labeled data. This study proposes a novel ROI extraction method employing a fully convolutional network and channel-spatial attention (CSFCN) for hyperspectral small targets through self-supervised learning. First, a strategy for pseudo-label assignment is designed based on the feature similarity and spatial continuity of hyperspectral images (HSIs). Second, an unsupervised segmentation model for HSIs is established, incorporating an attention mechanism based on FCN. Finally, ROIs are extracted based on segmentation results. The experimental results on two real-world HSIs, HSIa and HSIb, show that the proposed method can effectively improve the accuracy of extracted ROIs. The overall accuracy (OA) and the intersection over union (IOU) values of the proposed CSFCN reached 25.62%, 44.44% (HSIa), and 44.76%, 100% (HSIb), far exceeding the traditional unsupervised segmentation results. The superior experimental results demonstrate that the proposed method has promising application prospects.