A Survey on Object Detection in Optical Remote Sensing Images
Gong Cheng, Junwei Han

TL;DR
This survey reviews recent advances in generic object detection in optical remote sensing images, covering various methods, datasets, and evaluation metrics, and discusses future research directions like deep learning and weakly supervised learning.
Contribution
It provides a comprehensive overview of recent methods and datasets in remote sensing object detection, highlighting new research directions and gaps in current studies.
Findings
Survey covers 270 publications and multiple detection methods.
Identifies challenges and proposes future research directions.
Discusses datasets and evaluation metrics used in the field.
Abstract
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…
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