Full Range Feature Extraction Network for Hyperspectral Image Classification

Wenxiang Zhu, Deping Chen, Yinghui Quan, Na Li, Yongxu Liu

Abstract: Hyperspectral remote sensing images exhibit fine spectral curves, but they are also susceptible to spectral variations caused by factors like cloud and haze. Thus, a full range feature extraction network (FRFENet) is proposed for hyperspectral image classification. 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. Experiments on three HSI datasets indicate that the FRFENet can obtain better classification performance when compared with the other ten state-of-the-art methods.