Full-Range Feature Extraction Network Based on Quality–Quantity-Balance Sample Enhancement for Hyperspectral Image Classification

Hyperspectral remote sensing images exhibit fine spectral curves, but they are also susceptible to spectral variations caused by factors such as cloud and haze. It is evident that these issues become more pronounced when there is a limited number of labeled samples available. Thus, a full range feature extraction network (FRFENet) based on quality–quantity-balance sample enhancement is proposed for hyperspectral image classification (HSIC). First, the full-range feature extraction method combines local-range, short-range, and long-range spatial–spectral features to address spectral variability and ensure accurate feature extraction, particularly in scenarios with limited labeled samples. Furthermore, the approach of balancing quality and quantity for pseudolabeled samples allows for an increased number of pseudolabels while maintaining their quality, effectively leveraging unlabeled samples. In addition, the utilization of superpixel region homogeneity directly contributes to an expanded training sample set, resulting in improved classification performance of the algorithm. Experiments on three hyperspectral image (HSI) encompass datasets indicate that the FRFENet can obtain better classification performance when compared with the other ten state-of-the-art methods.