Segment Anything Model-Based Hyperspectral Image Classification for Small Samples

Hyperspectral image classification (HSIC) represents a significant area of research within the domain of remote sensing. Given the intricate nature of hyperspectral images and the substantial volume of data they generate, it is essential to introduce innovative methodologies to effectively address the data pre-processing challenges encountered in HSIC. In this paper, we draw inspiration from the Segment Anything Model (SAM) within the realm of large language models to propose its application for HSIC, aiming to achieve significant advancements and breakthroughs in this field. Initially, we constructed the SAM and labeled a limited number of samples as segmentation prompts for the model. We conducted HSIC experiments utilizing three publicly available hyperspectral image datasets: Indian Pines (IP), Salinas (SA), and Pavia University (PU). Furthermore, a voting strategy was implemented during these experiments, with only five samples selected from each land type. The classification results obtained from the SAM-based hyperspectral images were compared with those derived from eight distinct machine learning, deep learning, and Transformer models. The findings indicate that the SAM requires only a limited number of samples to effectively perform hyperspectral image classification, achieving higher accuracy than the other models discussed in this paper. Building on this foundation, a voting strategy was implemented, leading to significant enhancements in the overall accuracy (OA) of HSIC across three datasets. The improvements were quantified at 66.76%, 74.66%, and 70.53%, respectively, culminating in final accuracies of 80.29%, 90.66%, and 86.51%. In this study, the SAM is utilized for unsupervised classification, thereby reducing the need for sample labeling while attaining effective classification outcomes.