这篇论文给遥感图像分割的可解释性带来了新思路，用熵做核心，还配了新的评估方法，比现有方法效果更好。
该论文提出一种以熵为中心的可解释AI（XAI）方法，用于遥感图像的语义分割。作者还设计了一种新的XAI评估方法，以高效衡量所提方法高亮区域的相关性。实验结果表明，该方法在性能上优于近期为语义分割适配的XAI方法。研究旨在提升黑盒模型在遥感等关键领域的透明度和信任度。
Entropy-Centric Explainable AI for Remote Sensing Image Segmentation
Artificial intelligence (AI) has become a powerful approach to solving complex problems in critical domains. Many concerns arise regarding the decision-making process of its models, mainly due to deep neural networks outperforming their peers at the cost of ambiguity in feature extraction and prediction. Consequently, in critical domains such as remote sensing, where high-resolution imagery must be analyzed using black-box models, the lack of transparency limits trust in these models and, thus, their adoption. In light of this reality, explaining and understanding the complex decision-making process of AI models has become essential. Explainable AI (XAI) aims to bridge this gap by providing insights into how and why certain decisions are made. While significant progress has been achieved in explaining image classification tasks, image segmentation still offers considerable room for improvement. In this context, this paper proposes an entropy-centric XAI method for semantic segmentation. Moreover, a new XAI evaluation methodology is proposed to efficiently measure the relevance of the regions highlighted by the proposed XAI method. Experimental results demonstrate the superiority of the proposed XAI method compared with recently adapted XAI methods for semantic segmentation.