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Open AccessArticle

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

1
School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China
2
School of Geosciences and Info-Physics, Central South University, Changsha 410083, China
3
College of Telecommunication and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
*
Author to whom correspondence should be addressed.
Academic Editors: Lizhe Wang, Josef Kellndorfe, Xiaofeng Li and Prasad S. Thenkabail
Remote Sens. 2016, 8(9), 709; https://doi.org/10.3390/rs8090709
Received: 3 June 2016 / Revised: 19 August 2016 / Accepted: 24 August 2016 / Published: 27 August 2016
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. View Full-Text
Keywords: high-resolution remote sensing image management; content-based high-resolution remote sensing image retrieval (CB-HRRS-IR); unsupervised feature learning; collaborative affinity metric fusion high-resolution remote sensing image management; content-based high-resolution remote sensing image retrieval (CB-HRRS-IR); unsupervised feature learning; collaborative affinity metric fusion
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MDPI and ACS Style

Li, Y.; Zhang, Y.; Tao, C.; Zhu, H. Content-Based High-Resolution Remote Sensing Image Retrieval via Unsupervised Feature Learning and Collaborative Affinity Metric Fusion. Remote Sens. 2016, 8, 709. https://doi.org/10.3390/rs8090709

AMA Style

Li Y, Zhang Y, Tao C, Zhu H. Content-Based High-Resolution Remote Sensing Image Retrieval via Unsupervised Feature Learning and Collaborative Affinity Metric Fusion. Remote Sensing. 2016; 8(9):709. https://doi.org/10.3390/rs8090709

Chicago/Turabian Style

Li, Yansheng; Zhang, Yongjun; Tao, Chao; Zhu, Hu. 2016. "Content-Based High-Resolution Remote Sensing Image Retrieval via Unsupervised Feature Learning and Collaborative Affinity Metric Fusion" Remote Sens. 8, no. 9: 709. https://doi.org/10.3390/rs8090709

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Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

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