Unveiling decision-making in remote sensing with deep learning interpretability

Deep neural networks have emerged as the predominant technical approach for remote sensing image interpretation and processing, surpassing traditional methods in various tasks such as target extraction, classification, and recognition. However, the decision-making processes underlying these deep networks usually lack transparency, making interpretability a pressing concern. In response to this concern, we employ four prominent feature attribution methods, namely Integrated Gradients, GradientShap, Occlusion, and Saliency, to perform interpretability analysis on deep learning models designed for visible light remote sensing image classification. Our objective is to unveil the foundational principles guiding the decision-making in remote sensing image classification and recognition. We also assess the effectiveness of these attribution methods in identifying crucial decision regions. Through our visual attribution analysis, we aim to contribute to a better understanding of the decision-making mechanisms employed by remote sensing image classification models.