Sample Augmentation With Threshold Estimation for Classification With Hyperspectral Remote Sensed Image

Sample augmentation is crucial for improving land-cover classification performance when the samples are limited. However, the traditional sample augmentation approach concentrates on enlarging the quantity of sample via generation and synthetic technique directly, the sample quality is usually neglected. In this article, we propose a novel sample augmentation approach with threshold estimation (SATE) to improve both the quantity and quality of samples for hyperspectral remotely sensed image (HRSI) classification. First, a threshold estimation algorithm (TEA) is proposed to identify high-confidence potential samples from the initial classification map by utilizing the prediction probabilities of different classes. Second, a semi-variational model is employed to detect and correct pseudo-labels in the spatial domain, further enhancing the quality of selected potential samples. Finally, a farthest point sampling (FPS) algorithm optimizes sample distribution in the spectral domain, improving representation for intraclass heterogeneity. Experimental results based on four real HRSIs and compared with eight state-of-the-art few-shot-based methods verify the feasibility and superiority of the proposed SATE approach. The improvement achieved by our proposed approach is about 0.79%–4.31% in terms of the overall accuracy (OA). The code is available at https://github.com/ImgSciGroup/SATE