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A Self-attention Feature Metric Learning Method for Remote Sensing Image Retrieval

Published: 07 September 2023 Publication History

Abstract

Remote sensing image retrieval (RSIR) is a challenging task due to the complex backgrounds of the image and the inclusion of various semantic objects. This paper presents a novel self-attention mechanism for RSIR using deep learning networks. We elaborate on the long-distance and the multi-layer dependence of image regions within a convolutional neural network to extract the salient features of remote sensing image against a complex background. We propose a new learning network structure of a multiple similarity (MS) loss function to further enhance the discriminability of features. The experiments on the typical datasets show that our method can significantly enhance the accuracy of RSIR. Moreover, when self-attention mechanism and MS loss function are simultaneously considered in the network, the retrieval precision is better than the results obtained by the previous methods.

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        ICMLC '23: Proceedings of the 2023 15th International Conference on Machine Learning and Computing
        February 2023
        619 pages
        ISBN:9781450398411
        DOI:10.1145/3587716
        Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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        Published: 07 September 2023

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        Author Tags

        1. CNN
        2. Deep learning
        3. Multiple similarity loss function
        4. Remote sensing image
        5. Self-attention mechanism

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