Hyperspectral Image Classification in Remote Sensing Using CNNs and Attention Modules

Abstract

In the field of remote sensing, the classification of hyperspectral images (HSI) has gained popularity. Convolutional neural networks (CNNs) are a potent visual model that have attracted a lot of attention recently due to their outstanding performance in a variety of visual recognition problems. However, CNNs' performance may be limited if they can't fully utilize the extensive spectral information contained in hyperspectral images. One approach to overcome this limitation is to incorporate attention mechanisms into the CNN architecture. Attention allows the model to focus on the most relevant parts of the input, enabling it to better capture the spectral characteristics of different materials. This chapter focuses on all the studies that are carried out with the convolutional neural network and attention module approach on the hyperspectral image classification in remote sensing between 2012 and 2022. The major objective of this study is to review, identify, evaluate, and analyze the performance of CNN models and attention module in hyperspectral image classification.