An intelligent classification method for remote sensing images based on hyperspectral imaging technology

High-quality remote sensing images can be obtained by utilizing hyperspectral imaging technology. The task of detecting features can be realized by analyzing the rich spectral information contained in hyperspectral remote sensing images(HRSI), and categorizing features is an important research direction of hyperspectral technology in the field of remote sensing. However, due to spectral variations, noise interference, and spectral mixing among different features, it is challenging to extract the category information of feature targets from HRSI. In recent years, deep learning has performed well in image classification tasks and can effectively extract deep features of targets. Therefore, we propose an intelligent classification method for HRSI based on hyperspectral imaging technology by combining convolutional neural network and attention mechanism to extract the spatial and spectral features of HRSI, which realizes the accurate classification of feature targets in HRSI. Experiments have shown that our method can achieve superior classification performance compared with existing methods.