INTERPRETING HYPERSPECTRAL REMOTE SENSING IMAGE CLASSIFICATION METHODS VIA EXPLAINABLE ARTIFICIAL INTELLIGENCE

This study addresses the explainability challenges of deep-learning models in the context of hyperspectral remote sensing image classification. Three prominent explainable artificial intelligence methods, namely GradCAM, GradCAM++, and Guided Backpropagation, have been employed in order to comprehend the decision-making process of a typical convolutional neural network model during spatial-spectral hyperspectral image classification. The experiments that have been conducted investigate the impact of pixel patch sizes on spatial attention, as well as spectral band importance. The findings provide insights into the behavior of both convolutional neural networks, as well as the comparative performance of explainability techniques.