TY - JOUR
T1 - Opening the Black Box
T2 - A systematic review on explainable artificial intelligence in remote sensing
AU - Hohl, Adrian
AU - Obadic, Ivica
AU - Fernandez-Torres, Miguel Angel
AU - Najjar, Hiba
AU - Oliveira, Dario Augusto Borges
AU - Akata, Zeynep
AU - Dengel, Andreas
AU - Zhu, Xiao Xiang
N1 - Publisher Copyright:
© 2024 The Authors.
PY - 2024
Y1 - 2024
N2 - 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 explainable AI methods used and their objectives, findings, and challenges in remote sensing applications is still missing. In this paper, we address this gap by performing a systematic review to identify the key trends in the field 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, and reflect on the approaches used for the evaluation of explainable AI methods. As such, our review provides a complete summary of the state-of-the-art of explainable AI in remote sensing. 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.
AB - 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 explainable AI methods used and their objectives, findings, and challenges in remote sensing applications is still missing. In this paper, we address this gap by performing a systematic review to identify the key trends in the field 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, and reflect on the approaches used for the evaluation of explainable AI methods. As such, our review provides a complete summary of the state-of-the-art of explainable AI in remote sensing. 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.
UR - http://www.scopus.com/inward/record.url?scp=85208787024&partnerID=8YFLogxK
U2 - 10.1109/MGRS.2024.3467001
DO - 10.1109/MGRS.2024.3467001
M3 - Review article
AN - SCOPUS:85208787024
SN - 2473-2397
VL - 12
SP - 261
EP - 304
JO - IEEE Geoscience and Remote Sensing Magazine
JF - IEEE Geoscience and Remote Sensing Magazine
IS - 4
ER -