Geometric-Spectral Reconstruction Learning for Multi-Source Open-Set Classification With Hyperspectral and LiDAR Data

Abstract

Dear Editor, This letter presents an open-set classification method of remote sensing images (RSIs) based on geometric-spectral reconstruction learning. More specifically, in order to improve the ability of RSI classification model to adapt to the open-set environment, an open-set classification method based on geometric and spectral feature fusion is proposed. This method proposes to realize RSI open-set classification based on geometric and spectral features with hyper-spectral and light detection and ranging (LiDAR) data for the first time. In a variety of data sources of remote sensing, hyperspectral images (HSIs) and LiDAR data can provide rich spectral and geometric information for target objects. This letter combines both HSIs and LiDAR data to realize the recognition of unknown classes and the classification of known classes. Experiments show that the proposed method is better than previous state-of-the-art methods.