Content-Based High-Resolution Remote Sensing Image Retrieval via Unsupervised Feature Learning and Collaborative Affinity Metric Fusion

By Yansheng Li, Yongjun Zhang, Chao Tao, Hu Zhu. 2016

71 citations recorded by the source corpus.

With the urgent demand for automatic management of large numbers of high-resolution remote sensing images, content-based high-resolution remote sensing image retrieval (CB-HRRS-IR) has attracted much research interest. Accordingly, this paper proposes a novel high-resolution remote sensing image retrieval approach via multiple feature representation and collaborative affinity metric fusion (IRMFRCAMF). In IRMFRCAMF, we design four unsupervised convolutional neural networks with different layers to generate four types of unsupervised features from the fine level to the coarse level. In addition to these four types of unsupervised features, we also implement four traditional feature descriptors, including local binary pattern (LBP), gray level co-occurrence (GLCM), maximal response 8 (MR8), and scale-invariant feature transform (SIFT). In order to fully incorporate the complementary information among multiple features of one image and the mutual information across auxiliary images in the image dataset, this paper advocates collaborative affinity metric fusion to measure the similarity between images. The performance evaluation of high-resolution remote sensing image retrieval is implemented on two public datasets, the UC Merced (UCM) dataset and the Wuhan University (WH) dataset. Large numbers of experiments show that our proposed IRMFRCAMF can significantly outperform the state-of-the-art approaches.

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. Gong Cheng
2. Junwei Han
3. Xiaoqiang Lu
4. Yakoub Bazi
5. Nassim Ammour
6. Naif Alajlan
7. Zhenfeng Shao
8. Weixun Zhou

In Media Technology

1. Gong Cheng
2. Junwei Han
3. Xiaoqiang Lu
4. Nassim Ammour
5. Yakoub Bazi
6. Naif Alajlan
7. Zhenfeng Shao
8. Weixun Zhou

Citations in the corpus

• Remote Sensing Image Scene Classification: Benchmark and State of the Art (2017, background): “wide range of applications, such as natural hazards detection [5-7], LULC determination [8-43], geospatial object detection [27, 44-52], geographic image retrieval [53-63], vegetation mapping [64-68], environment monitoring, and 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).”
• 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].”