用信息熵设计可解释模型，提升遥感图像分割透明度
Entropy-Centric Explainable AI for Remote Sensing Image Segmentation
- 以信息熵为核心构建可解释性分析框架
- 新评估方法有效验证了关键区域的突出效果
- 适合需要高可信度决策的遥感分析场景
人工智能在关键领域解决复杂问题能力强大，但其深度神经网络在特征提取和预测中的模糊性引发信任危机。尤其在遥感领域，高分辨率影像分析依赖黑箱模型，缺乏透明度严重制约应用。为此，本文提出一种基于信息熵的可解释人工智能（XAI）方法，用于语义分割任务，并设计新的XAI评估方法，高效衡量所提方法标注区域的相关性。实验表明，该方法在语义分割任务中优于近期已有的可解释方法。
原文摘要 · Abstract (English)
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.
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