Deep learning has taken by storm all fields involved in data analysis, including remote sensing for Earth observation. However, despite significant advances in terms of performance, its lack of explainability and interpretability, inherent to neural networks in general since their inception, remains a major source of criticism. Hence it comes as no surprise that the expansion of deep learning methods in remote sensing is being accompanied by increasingly intensive efforts oriented towards addressing this drawback through the exploration of a wide spectrum of Explainable Artificial Intelligence techniques. This chapter, organized according to prominent Earth observation application fields, presents a panorama of the state-of-the-art in explainable remote sensing image analysis.

翻译：深度学习已席卷包括地球观测遥感在内的所有数据分析领域。然而，尽管在性能方面取得了显著进展，但其缺乏可解释性与可解释性——自神经网络诞生以来普遍存在的固有缺陷——仍是主要的批评来源。因此，深度学习在遥感领域的扩展必然伴随着日益密集的努力，旨在通过探索广泛的可解释人工智能技术来应对这一缺陷。本章节依据主要的地球观测应用领域组织，系统呈现了可解释遥感图像分析的最新研究全景。