Enhancing Transparency of Neural Networks for Super-Resolution in Remote Sensing Using Local Attribution Maps

As deep learning models advance, their use in Super Resolution (SR) tasks has become pivotal for enhancing remote sensing low-resolution (LR) satellite images. However, the decision making processes within these models remain opaque, especially in remote sensing applications where transparency is critical. This paper focuses on applying Explainable Artificial Intelligence (XAI) techniques, particularly Local Attribution Maps (LAMs), to analyze and interpret the internal behavior of both general purpose and remote sensing specific SR neural networks. General purpose models like Generative Adversarial Network for Super Resolution (SRGAN), Enhanced Deep Super-Resolution Network (EDSR), Efficient Super-Resolution Transformer (ESRT), and Hybrid-Attention Transformer (HAT), although highly effective in SR tasks, were originally designed for broader image enhancement challenges; in contrast, Hybrid-Scale Self-Similarity Exploitation Network (HSENet) and Multi-scale Enhanced Network (MEN) are tailored for the unique complexities of remote sensing, such as varied textures and intricate scene features. By leveraging LAMs, we highlight how different networks prioritize and process features such as edges, textures, and high frequency details to generate super resolved outputs. The comparative study between general purpose and remote sensing specific networks outlines each model’s strengths and weaknesses in managing the data present in remote sensing imagery. This approach addresses the previously unexplored application of LAMs in remote sensing for SR, contributing to the research in this field and providing deeper insights into the interpretability and transparency of widely used SR models. Furthermore, by drawing parallels between feature attribution in SR and classification tasks, we suggest new pathways for integrating semantic information to refine model transparency and performance in remote sensing applications.