面向遥感图像分割的以熵为核心的可解释人工智能

中文摘要 AI 辅助

人工智能（AI）已成为解决关键领域复杂问题的强大方法，但其模型的决策过程引发诸多担忧，主要原因是深度神经网络在性能超越同类模型的同时，特征提取与预测存在模糊性。在遥感等关键领域，需使用黑箱模型分析高分辨率图像，透明度的缺失限制了对这些模型的信任，进而阻碍了其应用。鉴于此，解释和理解AI模型的复杂决策过程变得至关重要，可解释人工智能（XAI）旨在提供决策方式与原因的洞见，弥合这一差距。尽管图像分类任务的解释已取得显著进展，但图像分割领域仍有较大提升空间。在此背景下，本文提出一种以熵为核心的语义分割XAI方法，还提出一种新的XAI评估方法，用于高效衡量该方法突出区域的相关性。实验结果表明，与近期适配的语义分割XAI方法相比，所提XAI方法具有优越性。

英文摘要

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.

发表机构

• Faculty of Engineering, Lebanese University（黎巴嫩大学工程学院）
• University of Paris-Est Créteil (UPEC)（巴黎东部克雷泰伊大学）
• EFREI（EFREI学院）
• National Center for Remote Sensing - CNRS（国家遥感中心 - 法国国家科学研究中心）

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