Novel Land-Cover Classification Approach With Nonparametric Sample Augmentation for Hyperspectral Remote-Sensing Images

Samples play a crucial role in the supervised classification of remote-sensing images. However, labeling large samples for training a classifier or deep-learning network is not only time-consuming, but also labor-intensive. In this article, a novel land-cover classification with nonparametric sample augmentation is proposed to improve the performance of hyperspectral remote-sensing image (HRSI) classification. First, initial samples with limited quantity are selected randomly from the ground-truth map. Second, based on the gray image, a nonparametric adaptive region generation (NARG) algorithm is developed for utilizing the contextual information around each sample. Then, a nonparametric sample augmentation algorithm is developed with NARG to explore reliable samples iteratively around each initial sample. Finally, the above steps are fused into an iterative process to obtain the final classification map. Compared with some typical traditional methods and some widely used deep-learning methods based on four real HRSIs, our proposed approach exhibits some advantages in improving the visual performance and quantitative accuracies of HRSI classification, such as the improvement is about 2.0%-10.34% for four real HRSIs in terms of the overall accuracy (OA).