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Entropy-Centric Explainable AI for Remote Sensing Image Segmentation — arXiv:2608.11064

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.11064
License (as archived): CC0-1.0
Archived: 2026-10-19T06:09:39.638Z
SHA-256 of the archived text: 82aca63abfbc59ac3547cd2f4560c153afbfc458b19bf875d7a2fb73211f6efb
Stable URL of this page: https://metraipolis.com/documento/dc_7fec615eb7c2

How to cite

Entropy-Centric Explainable AI for Remote Sensing Image Segmentation — arXiv:2608.11064 — arXiv (en), https://arxiv.org/abs/2608.11064 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_7fec615eb7c2 (Metraipolis archive dc_7fec615eb7c2, sha256 82aca63abfbc59ac3547cd2f4560c153afbfc458b19bf875d7a2fb73211f6efb)

BibTeX

@misc{dc7fec615eb7c2,
  title = {Entropy-Centric Explainable AI for Remote Sensing Image Segmentation — arXiv:2608.11064},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.11064},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_7fec615eb7c2; sha256 82aca63abfbc59ac3547cd2f4560c153afbfc458b19bf875d7a2fb73211f6efb},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.11064. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

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.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-08-12 in cs.AI).
Categorie arXiv: Computer Vision and Pattern Recognition (cs.CV) ; Artificial Intelligence (cs.AI).
Pagina del lavoro: https://arxiv.org/abs/2608.11064 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-19T05:57:28.429Z dalla pagina https://arxiv.org/catchup/cs.AI/2026-08-12?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).