A Three-Layered Graph-Based Learning Approach for Remote Sensing Image Retrieval

By Yuebin Wang, Liqiang Zhang, Xiaohua Tong, L. Zhang, Zhenxin Zhang, Hao Liu, Xiaoyue Xing, P. Mathiopoulos. 2016

82 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Media Technology

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Junwei Han
2. Gong Cheng
3. Nassim Ammour
4. Lei Guo
5. Guisong Xia
6. Xuelong Li
7. Yakoub Bazi
8. Peicheng Zhou

In Media Technology

1. Gong Cheng
2. Junwei Han
3. Nassim Ammour
4. Lei Guo
5. Guisong Xia
6. Yakoub Bazi
7. Xuelong Li
8. Jocelyn Chanussot

Citations in the corpus

• Classification of Remote Sensing Images Using EfficientNet-B3 CNN Model With Attention (2021, background): “…survey, urban planning [6], nat-ural resources monitoring, weather forecasting, land-cover/ The associate editor coordinating the review of this manuscript and approving it for publication was Jeon Gwanggil . land-use classification and geographic space object detection/ retrieval [7], [8].”
• Scene Classification With Recurrent Attention of VHR Remote Sensing Images (2019, background): “increasingly available and bringing us the opportunity to try more research in military and civilian applications, such as natural disaster detection [3], [4], land-cover/land-use classification [5], [6] geographic space object detection [7], [8], geographic image retrieval [9], [10], urban planning”
• PatternNet: A Benchmark Dataset for Performance Evaluation of Remote Sensing Image Retrieval (2017, background): “Since then, a number of unsupervised feature learning approaches have been proposed for various remote sensing applications like RSIR (Zhou et al., 2015; Wang et al., 2016; Li et al., 2016).”
• Remote Sensing Image Scene Classification Meets Deep Learning: Challenges, Methods, Benchmarks, and Opportunities (2020, background): “ew decades, extensive researches on satellite image scene classification have been undertaken driven by its real-world applications, such as urban planning [5], [6], remote sensing image retrieval [7]–[9], natural hazards detection [10]–[12], environment monitoring [13], [14], vegetation mapping [15]”