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 providi...
| Authors | Ali Saleh, Abdul Karim Gizzini, Mohamad Ghassany et al. |
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| Primary category | cs.CV |
| Published | 11 August 2026 |
| Last updated at source | 11 August 2026 |
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