Self-Supervised Hierarchical Dilated Transformer Network for Hyperspectral Soil Microplastic Identification and Detection

Microplastics are plastic particles less than five millimeters in diameter that have led to serious environmental problems, and detecting these tiny particles is crucial to understanding their distribution and impact on the soil environment. In this paper, we propose the Self-Supervised Hierarchical Dilated Transformer Network (SHDTNet), an improved hyperspectral image classification model based on self-supervised contrastive learning, for identifying and detecting microplastics in soil. Currently, most hyperspectral image classifications rely on supervised methods, which perform well with rich training samples. However, pixel labeling in soil microplastic detection scenarios is a difficult and costly task. By employing the self-supervised contrastive learning technique, SHDTNet addresses the problem of insufficient training samples for hyperspectral images of soil microplastics and also enhances the feature extraction module in contrastive learning to improve the network model's feature extraction capability. Experiments on self-constructed hyperspectral soil microplastic image datasets demonstrate that the proposed method accurately recognizes unique microplastics in the soil environment without errors or missed detections, outperforming several currently available soil microplastic detection methods.