A Survey on Object Detection in Optical Remote Sensing Images

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 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.