A Survey on Object Detection in Optical Remote Sensing Images
By Gong Cheng, Junwei Han. 2016
1,336 citations recorded by the source corpus.
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.
Subfields: Computer Vision and Pattern Recognition, Media Technology, Ocean Engineering
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Citations in the corpus
- Nested Network With Two-Stream Pyramid for Salient Object Detection in Optical Remote Sensing Images (2019, methodology): “Similar to the related works [8]–[12], the remote sensing images (RSIs) used in this paper were collected from Google Earth with the spatial resolution ranging from 0.”
- Deep Learning for Change Detection in Remote Sensing Images: Comprehensive Review and Meta-Analysis (2020, methodology): “Much effort also needs to be made to establish more efficient methods to improve the detection accuracy [126].”
- Learning Rotation-Invariant Convolutional Neural Networks for Object Detection in VHR Optical Remote Sensing Images (2016, background): “A recent review on object detection in optical remote sensing images can be found in [26].”
- YOLO-based Object Detection Models: A Review and its Applications (2024, positive): “Other datasets that are used in previous research work include Remote sensing target detection [42] [43] [44], text detection [45] [46] [47], and Pedestrian detection [48] [49] [50].”
- Oriented RepPoints for Aerial Object Detection (2021, methodology): “2) Evaluation Metric: In addition to the traditional evaluation metric mean Average Precision (mAP) on overlap ratio [8], [16], [46], we suggest a novel measure mean Average Orientation Error (mAOE) to take into consideration of the orientation differences for the arbitrary-oriented object detectors”
- FAIR1M: A Benchmark Dataset for Fine-grained Object Recognition in High-Resolution Remote Sensing Imagery (2021, background): “It is one of the most fundamental and important tasks in the field of earth observation, which serves various civil applications, such as geographic information sys-tem mapping, agriculture, traffic planning, and navigation [53, 52, 19, 50, 49, 7, 6, 11].”
- RSPrompter: Learning to Prompt for Remote Sensing Instance Segmentation Based on Visual Foundation Model (2023, background): “The applications of this technology span a multitude of domains, including, but not limited to, national defense, land surveying, disaster monitoring, and traffic planning [7], [10], [11], [12], [13], [14], [15], [16].”
- Accurate Object Localization in Remote Sensing Images Based on Convolutional Neural Networks (2017, background): “Cheng and Han [7] provided a review of the recent progress in object detection in remote sensing images and proposed two promising research directions, namely, deep learning-based feature representation and weakly supervised learning-based geospatial object detection.”
- Incremental Detection of Remote Sensing Objects With Feature Pyramid and Knowledge Distillation (2020, background): “Object detection in remote sensing plays a crucial role in a wide scope of applications, such as intelligent monitoring, urban planning, and precision agriculture [1].”
- Object Detection in 20 Years: A Survey (2019, background): “We refer readers to the following surveys for more details on this topic [90, 382].”
- Object Detection in Optical Remote Sensing Images: A Survey and A New Benchmark (2019, neutral): “ In our work, we first investigated the object classes of all existing datasets (Benedek et al., 2011; Cheng and Han, 2016; Cheng et al., 2016a; Heitz and Koller, 2008; Liu and Mattyus, 2015; Liu et al., 2016b; Razakarivony and …”
- Scene classification of high resolution remote sensing images using convolutional neural networks (2016, background): “Scene classification of high resolution remote sensing images plays an important role for a wide range of applications, such as land-use determination, natural hazards detection, urban planning, geospatial object detection and so on [4-17].”
- Towards Large-Scale Small Object Detection: Survey and Benchmarks (2022, background): “Quite a number of surveys about object detection have been published in recent years [13], [14], [15], [16], [17], [18], [19], [20], [21], [22], [23], [24], and our review differs from the existing ones mainly in two aspects.”