Remote Sensing Image Retrieval in the Past Decade: Achievements, Challenges, and Future Directions

Remote sensing image retrieval (RSIR) aims to search and retrieve the images of interest from a large remote sensing image archive, which has remained to be a hot topic over the past decade. Benefited from the advent and progress of deep learning, RSIR has been promoted by developing novel approache...

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Bibliographic Details
Published in:IEEE journal of selected topics in applied earth observations and remote sensing 2023, Vol.16, p.1447-1473
Main Authors: Zhou, Weixun, Guan, Haiyan, Li, Ziyu, Shao, Zhenfeng, Delavar, Mahmoud R.
Format: Article
Language:English
Subjects:
Benchmark testing
Benchmarks
Classification
Datasets
Deep learning
Feature extraction
Image retrieval
literature review
Methods
Optical sensors
Performance evaluation
Remote sensing
remote sensing image retrieval (RSIR)
similarity measure
Task analysis
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Summary:Remote sensing image retrieval (RSIR) aims to search and retrieve the images of interest from a large remote sensing image archive, which has remained to be a hot topic over the past decade. Benefited from the advent and progress of deep learning, RSIR has been promoted by developing novel approaches, constructing new datasets, and exploring potential applications. To the best of our knowledge, there lacks a comprehensive review of RSIR achievements, including systematic and hierarchical categorization of RSIR methods and benchmark datasets over the past decade. This article, therefore, provides a systematic survey of the recently published RSIR methods and benchmarks by reviewing more than 200 papers. To be specific, in terms of image source, label, and modality, we first group the RSIR methods into some hierarchical categories, each of which is reviewed in detail. Following the categorization of the RSIR methods, we list the benchmark datasets publicly available for performance evaluation and present our newly collected RSIR dataset. Moreover, some of the existing RSIR methods are selected and evaluated on the representative benchmark datasets. The results demonstrate that deep learning-based methods are currently the dominant RSIR approaches and outperform handcrafted feature-based methods by a significant margin. Finally, we discuss the main challenges of RSIR and point out some potential directions for the future RSIR research.
ISSN:1939-1404
2151-1535
DOI:10.1109/JSTARS.2023.3236662