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

• Published in: ISPRS International Journal of Geo-Information
• Main Authors: Hongwei Zhao, Lin Yuan, Haoyu Zhao
• Format: Article in Journal/Newspaper
• Language: English
• Published: MDPI AG 2020
• Subjects: content-based remote sensing image retrieval (cbrsir); deep metric learning (dml); structural ranking consistency; Geography (General); G1-922; DML
• Online Access: https://doi.org/10.3390/ijgi9020061; https://doaj.org/article/d6d1c335f4024367bfa87e9c28e88b60

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.