Opening the Black-Box: A Systematic Review on Explainable AI in Remote Sensing

• URL: http://arxiv.org/abs/2402.13791v1
• Date: Wed, 21 Feb 2024 13:19:58 GMT
• Title: Opening the Black-Box: A Systematic Review on Explainable AI in Remote Sensing
• Authors: Adrian H\"ohl, Ivica Obadic, Miguel \'Angel Fern\'andez Torres, Hiba Najjar, Dario Oliveira, Zeynep Akata, Andreas Dengel, Xiao Xiang Zhu
• Abstract summary: Black-box machine learning approaches have become a dominant modeling paradigm for knowledge extraction in Remote Sensing. We perform a systematic review to identify the key trends of how explainable AI is used in Remote Sensing. We shed light on novel explainable AI approaches and emerging directions that tackle specific Remote Sensing challenges.
• Score: 52.110707276938
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In recent years, black-box machine learning approaches have become a dominant modeling paradigm for knowledge extraction in Remote Sensing. Despite the potential benefits of uncovering the inner workings of these models with explainable AI, a comprehensive overview summarizing the used explainable AI methods and their objectives, findings, and challenges in Remote Sensing applications is still missing. In this paper, we address this issue by performing a systematic review to identify the key trends of how explainable AI is used in Remote Sensing and shed light on novel explainable AI approaches and emerging directions that tackle specific Remote Sensing challenges. We also reveal the common patterns of explanation interpretation, discuss the extracted scientific insights in Remote Sensing, and reflect on the approaches used for explainable AI methods evaluation. Our review provides a complete summary of the state-of-the-art in the field. Further, we give a detailed outlook on the challenges and promising research directions, representing a basis for novel methodological development and a useful starting point for new researchers in the field of explainable AI in Remote Sensing.

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