A Novel Self-Supervised Cross-Modal Image Retrieval Method In Remote Sensing

• URL: http://arxiv.org/abs/2202.11429v1
• Date: Wed, 23 Feb 2022 11:20:24 GMT
• Title: A Novel Self-Supervised Cross-Modal Image Retrieval Method In Remote Sensing
• Authors: Gencer Sumbul, Markus M\"uller, Beg\"um Demir
• Abstract summary: Cross-modal RS image retrieval methods search semantically similar images across different modalities. Existing CM-RSIR methods require annotated training images and do not concurrently address intra- and inter-modal similarity preservation and inter-modal discrepancy elimination. We introduce a novel self-supervised cross-modal image retrieval method that aims to model mutual-information between different modalities in a self-supervised manner.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Due to the availability of multi-modal remote sensing (RS) image archives, one of the most important research topics is the development of cross-modal RS image retrieval (CM-RSIR) methods that search semantically similar images across different modalities. Existing CM-RSIR methods require annotated training images (which is time-consuming, costly and not feasible to gather in large-scale applications) and do not concurrently address intra- and inter-modal similarity preservation and inter-modal discrepancy elimination. In this paper, we introduce a novel self-supervised cross-modal image retrieval method that aims to: i) model mutual-information between different modalities in a self-supervised manner; ii) retain the distributions of modal-specific feature spaces similar; and iii) define most similar images within each modality without requiring any annotated training images. To this end, we propose a novel objective including three loss functions that simultaneously: i) maximize mutual information of different modalities for inter-modal similarity preservation; ii) minimize the angular distance of multi-modal image tuples for the elimination of inter-modal discrepancies; and iii) increase cosine similarity of most similar images within each modality for the characterization of intra-modal similarities. Experimental results show the effectiveness of the proposed method compared to state-of-the-art methods. The code of the proposed method is publicly available at https://git.tu-berlin.de/rsim/SS-CM-RSIR.

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