---
格式版本: 2
标题: "Entropy-Centric Explainable AI for Remote Sensing Image Segmentation"
原文链接: "https://arxiv.org/abs/2608.11064"
发布日期: "2026-08-11"
发布时间校准状态: "found"
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发布时间校准时间: "2026-08-12T11:28:59+08:00"
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发现时间: "2026-08-12T11:26:18+08:00"
入库时间: "2026-08-12T03:29:00.085Z"
来源平台: "arXiv 学术论文搜索"
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搜索词: "https://arxiv.org/search/?query=AI&searchtype=all"
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AI优质: "否"
AI打分: 5
AI分档: "非优质"
AI质检状态: "不通过"
AI打分理由: "该论文讨论遥感图像分割的可解释AI，与超节点/AI Rack/机柜级AI基础设施完全无关，无任何技术或商业信号。"
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采集批次: "2026年8月12日2点04分20秒"
采集批次ID: "20260812-020420-289"
去重键: "https://arxiv.org/abs/2608.11064"
---

## Computer Science > Computer Vision and Pattern Recognition

## Title:Entropy-Centric Explainable AI for Remote Sensing Image Segmentation

[View PDF](https://arxiv.org/pdf/2608.11064) [HTML (experimental)](https://arxiv.org/html/2608.11064v1)

> Abstract: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.

| Subjects: | Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI) |
| --- | --- |
| Cite as: | [arXiv:2608.11064](https://arxiv.org/abs/2608.11064) \[cs.CV\] |
|  | (or [arXiv:2608.11064v1](https://arxiv.org/abs/2608.11064v1) \[cs.CV\] for this version) |
|  | [https://doi.org/10.48550/arXiv.2608.11064](https://doi.org/10.48550/arXiv.2608.11064) |

## Submission history

From: Ali J. Ghandour \[[view email](https://arxiv.org/show-email/473fd952/2608.11064)\]  
**\[v1\]** Tue, 11 Aug 2026 15:31:07 UTC (763 KB)

[Which authors of this paper are endorsers?](https://arxiv.org/auth/show-endorsers/2608.11064) | Disable MathJax ([What is MathJax?](https://info.arxiv.org/help/mathjax.html))
