Graph Sample and Aggregate-Attention Network for Hyperspectral Image Classification
摘要
Hyperspectral images (HSIs) provide detailed spectral information through hundreds of (narrow) spectral channels, which can be used to accurately classify diverse materials of interest (Rasti et al. in IEEE Geosci Remote Sens 8(4):60–88, 2020; Zhong et al. in IEEE Trans Neural Netw Learn Syst 12:1–13, 2019). However, the increased dimensionality of such data provides a challenge to conventional techniques, and hyperspectral classification has great research value.