A Sensitivity-Based Explainable Method for Remote Sensing Scene Classification

Deep learning models, widely used for their high accuracy in various applications such as remote sensing image classification, are frequently seen as black boxes due to their complex internal workings. Explainable artificial intelligence, a recent field of research, seeks to clarify the decision-making processes of these deep learning models, making them more transparent and comprehensible. In this study, a new model-agnostic explainable method based on sensitivity analysis is proposed. This method works by observing how the model's prediction changes when different parts of an image are perturbed using the meta-model representation. High Dimensional Model Representation is utilized as a meta-model due to its strengths in model approximation capability with a few feature interactions, allowing for efficient analysis and understanding of complex models with reduced computational complexity. The proposed approach is applied to a convolutional neural network model, specifically for tackling the remote sensing scene classification challenge using the EuroSAT dataset. The results are compared to those of the LIME method.