A survey on object detection in optical remote sensing images

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• 作者：Gong Cheng ; Junwei Han ; ^{Junweihan2010@gmail.com" class="auth_mail" title="E-mail the corresponding author}
• 关键词：Object detection ; Optical remote sensing images ; Template matching ; Object-based image analysis (OBIA) ; Machine learning ; Deep learning ; Weakly supervised learning
• 刊名：ISPRS Journal of Photogrammetry and Remote Sensing
• 出版年：2016
• 出版时间：July 2016
• 年：2016
• 卷：117
• 期：Complete
• 页码：11-28
• 全文大小：1449 K

文摘

Object detection in optical remote sensing images, being a fundamental but challenging problem in the field of aerial and satellite image analysis, plays an important role for a wide range of applications and is receiving significant attention in recent years. While enormous methods exist, a deep review of the literature concerning generic object detection is still lacking. This paper aims to provide a review of the recent progress in this field. Different from several previously published surveys that focus on a specific object class such as building and road, we concentrate on more generic object categories including, but are not limited to, road, building, tree, vehicle, ship, airport, urban-area. Covering about 270 publications we survey (1) template matching-based object detection methods, (2) knowledge-based object detection methods, (3) object-based image analysis (OBIA)-based object detection methods, (4) machine learning-based object detection methods, and (5) five publicly available datasets and three standard evaluation metrics. We also discuss the challenges of current studies and propose two promising research directions, namely deep learning-based feature representation and weakly supervised learning-based geospatial object detection. It is our hope that this survey will be beneficial for the researchers to have better understanding of this research field.