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Article

Similarity Retention Loss (SRL) Based on Deep Metric Learning for Remote Sensing Image Retrieval

by 1,2, 1,2 and 3,*
1
College of Computer Science and Technology, Jilin University, Changchun 130012, China
2
Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun 130012, China
3
Editorial Department of Journal (Engineering and Technology Edition), Jilin University, Changchun 130012, China
*
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2020, 9(2), 61; https://doi.org/10.3390/ijgi9020061
Received: 25 November 2019 / Revised: 11 January 2020 / Accepted: 19 January 2020 / Published: 21 January 2020
(This article belongs to the Special Issue Geographic Information Extraction and Retrieval)
Recently, with the rapid growth of the number of datasets with remote sensing images, it is urgent to propose an effective image retrieval method to manage and use such image data. In this paper, we propose a deep metric learning strategy based on Similarity Retention Loss (SRL) for content-based remote sensing image retrieval. We have improved the current metric learning methods from the following aspects—sample mining, network model structure and metric loss function. On the basis of redefining the hard samples and easy samples, we mine the positive and negative samples according to the size and spatial distribution of the dataset classes. At the same time, Similarity Retention Loss is proposed and the ratio of easy samples to hard samples in the class is used to assign dynamic weights to the hard samples selected in the experiment to learn the sample structure characteristics within the class. For negative samples, different weights are set based on the spatial distribution of the surrounding samples to maintain the consistency of similar structures among classes. Finally, we conduct a large number of comprehensive experiments on two remote sensing datasets with the fine-tuning network. The experiment results show that the method used in this paper achieves the state-of-the-art performance. View Full-Text
Keywords: content-based remote sensing image retrieval (CBRSIR); deep metric learning (DML); structural ranking consistency content-based remote sensing image retrieval (CBRSIR); deep metric learning (DML); structural ranking consistency
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MDPI and ACS Style

Zhao, H.; Yuan, L.; Zhao, H. Similarity Retention Loss (SRL) Based on Deep Metric Learning for Remote Sensing Image Retrieval. ISPRS Int. J. Geo-Inf. 2020, 9, 61. https://doi.org/10.3390/ijgi9020061

AMA Style

Zhao H, Yuan L, Zhao H. Similarity Retention Loss (SRL) Based on Deep Metric Learning for Remote Sensing Image Retrieval. ISPRS International Journal of Geo-Information. 2020; 9(2):61. https://doi.org/10.3390/ijgi9020061

Chicago/Turabian Style

Zhao, Hongwei; Yuan, Lin; Zhao, Haoyu. 2020. "Similarity Retention Loss (SRL) Based on Deep Metric Learning for Remote Sensing Image Retrieval" ISPRS Int. J. Geo-Inf. 9, no. 2: 61. https://doi.org/10.3390/ijgi9020061

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