Object Detection in Optical Remote Sensing Images: A Systematic Review of Methods, Benchmarks, and Operational Applications.
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| Title: | Object Detection in Optical Remote Sensing Images: A Systematic Review of Methods, Benchmarks, and Operational Applications. |
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| Authors: | Fontanet Garcia, Neus1 (AUTHOR), Boccardo, Piero1 (AUTHOR) piero.boccardo@polito.it |
| Source: | Remote Sensing. May2026, Vol. 18 Issue 9, p1289. 26p. |
| Subjects: | Optical remote sensing, Deep learning, Spatial variation, Real-time computing, Object recognition (Computer vision), Annotations, Benchmark problems (Computer science) |
| Abstract: | Highlights: What are the main findings? Deep learning methods achieve the highest detection accuracy (65–80% mAP) on standard remote sensing benchmarks, yet no single approach dominates across all performance dimensions—classical methods retain significant value for limited-data, interpretability, and resource-constrained scenarios. The three most critical operational bottlenecks identified are annotation cost (labour-intensive dense labelling), extreme scale variation spanning 2–3 orders of magnitude within single scenes, and domain adaptation failures causing 15–40% performance degradation across geographic regions. What are the implications of the main findings? Foundation models enabling zero-shot detection and weakly-supervised learning represent the highest-priority research directions, with the potential to reduce annotation requirements by up to 90% for time-critical applications such as disaster response. Hybrid architectures integrating deep learning with Object-Based Image Analysis and knowledge-based reasoning achieve 5–12% accuracy improvements over pure pixel-based CNNs, suggesting the future lies in complementary integration rather than single-methodology dominance. Object detection in optical remote sensing imagery has emerged as a crucial task in computer vision, with applications ranging between environmental monitoring to disaster management, precision agriculture, and urban planning. This review systematically examines current methodologies, categorising them into four principal approaches: (1) template matching-based methods, which leverage predefined patterns for object identification; (2) knowledge-based methods, which incorporate geometric and contextual information to enhance detection accuracy; (3) object-based image analysis (OBIA), which segments images into meaningful objects using spectral and spatial properties; (4) machine learning-based methods, particularly deep convolutional neural networks (CNNs), which have revolutionised the field through automatic feature learning. Each methodology's performance characteristics, computational requirements, and suitability for different remote sensing applications are analysed. Our systematic review, following PRISMA guidelines, analysed 189 studies published from 2010 to 2025, of which 73 provided quantitative results on standard benchmarks. The three most critical challenges identified are as follows: (1) annotation bottleneck, as dense bounding box labelling of remote sensing imagery remains highly labour-intensive for deep learning approaches, (2) extreme scale variation spanning 2–3 orders of magnitude within single scenes, and (3) domain adaptation failures when models encounter new geographic regions or sensor characteristics. This review identifies critical research gaps and proposes prioritised future directions, emphasising foundation models for zero-shot detection, efficient architectures for resource-constrained deployment, and standardised benchmarks with size-specific metrics. The analysis provides practitioners with evidence-based decision frameworks for method selection and researchers with a roadmap for advancing object detection in remote sensing applications. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | Highlights: What are the main findings? Deep learning methods achieve the highest detection accuracy (65–80% mAP) on standard remote sensing benchmarks, yet no single approach dominates across all performance dimensions—classical methods retain significant value for limited-data, interpretability, and resource-constrained scenarios. The three most critical operational bottlenecks identified are annotation cost (labour-intensive dense labelling), extreme scale variation spanning 2–3 orders of magnitude within single scenes, and domain adaptation failures causing 15–40% performance degradation across geographic regions. What are the implications of the main findings? Foundation models enabling zero-shot detection and weakly-supervised learning represent the highest-priority research directions, with the potential to reduce annotation requirements by up to 90% for time-critical applications such as disaster response. Hybrid architectures integrating deep learning with Object-Based Image Analysis and knowledge-based reasoning achieve 5–12% accuracy improvements over pure pixel-based CNNs, suggesting the future lies in complementary integration rather than single-methodology dominance. Object detection in optical remote sensing imagery has emerged as a crucial task in computer vision, with applications ranging between environmental monitoring to disaster management, precision agriculture, and urban planning. This review systematically examines current methodologies, categorising them into four principal approaches: (1) template matching-based methods, which leverage predefined patterns for object identification; (2) knowledge-based methods, which incorporate geometric and contextual information to enhance detection accuracy; (3) object-based image analysis (OBIA), which segments images into meaningful objects using spectral and spatial properties; (4) machine learning-based methods, particularly deep convolutional neural networks (CNNs), which have revolutionised the field through automatic feature learning. Each methodology's performance characteristics, computational requirements, and suitability for different remote sensing applications are analysed. Our systematic review, following PRISMA guidelines, analysed 189 studies published from 2010 to 2025, of which 73 provided quantitative results on standard benchmarks. The three most critical challenges identified are as follows: (1) annotation bottleneck, as dense bounding box labelling of remote sensing imagery remains highly labour-intensive for deep learning approaches, (2) extreme scale variation spanning 2–3 orders of magnitude within single scenes, and (3) domain adaptation failures when models encounter new geographic regions or sensor characteristics. This review identifies critical research gaps and proposes prioritised future directions, emphasising foundation models for zero-shot detection, efficient architectures for resource-constrained deployment, and standardised benchmarks with size-specific metrics. The analysis provides practitioners with evidence-based decision frameworks for method selection and researchers with a roadmap for advancing object detection in remote sensing applications. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 20724292 |
| DOI: | 10.3390/rs18091289 |
