Distribution Consistency Loss for Large-Scale Remote Sensing Image Retrieval

• Published in: Remote Sensing
• Main Authors: Lili Fan, Hongwei Zhao, Haoyu Zhao
• Format: Article in Journal/Newspaper
• Language: English
• Published: MDPI AG 2020
• Subjects: deep metric learning; remote sensing image retrieval (rsir); sample balance loss; distribution consistency loss; Science; Q; DML
• Online Access: https://doi.org/10.3390/rs12010175; https://doaj.org/article/fbb5cb20c4ef4d8da85d8b08275cf553

Remote sensing images are featured by massiveness, diversity and complexity. These features put forward higher requirements for the speed and accuracy of remote sensing image retrieval. The extraction method plays a key role in retrieving remote sensing images. Deep metric learning (DML) captures the semantic similarity information between data points by learning embedding in vector space. However, due to the uneven distribution of sample data in remote sensing image datasets, the pair-based loss currently used in DML is not suitable. To improve this, we propose a novel distribution consistency loss to solve this problem. First, we define a new way to mine samples by selecting five in-class hard samples and five inter-class hard samples to form an informative set. This method can make the network extract more useful information in a short time. Secondly, in order to avoid inaccurate feature extraction due to sample imbalance, we assign dynamic weight to the positive samples according to the ratio of the number of hard samples and easy samples in the class, and name the loss caused by the positive sample as the sample balance loss. We combine the sample balance of the positive samples with the ranking consistency of the negative samples to form our distribution consistency loss. Finally, we built an end-to-end fine-tuning network suitable for remote sensing image retrieval. We display comprehensive experimental results drawing on three remote sensing image datasets that are publicly available and show that our method achieves the state-of-the-art performance.