DLR-Logo -> http://www.dlr.de
DLR Portal Home | Imprint | Privacy Policy | Contact | Deutsch
Fontsize: [-] Text [+]

Attention-Driven Graph Convolution Network for Remote Sensing Image Retrieval

Chaudhuri, Ushashi and Banerjee, Biplab and Bhattacharya, Avik and Datcu, Mihai (2022) Attention-Driven Graph Convolution Network for Remote Sensing Image Retrieval. IEEE Geoscience and Remote Sensing Letters, p. 8019705. IEEE - Institute of Electrical and Electronics Engineers. doi: 10.1109/LGRS.2021.3105448. ISSN 1545-598X.

[img] PDF - Only accessible within DLR bis February 2023 - Published version

Official URL: https://ieeexplore.ieee.org/document/9526616


Graph convolution networks (GCNs) are useful in remote sensing (RS) image retrieval. It is found to be effective because, in a graph representation, the relative geometrical interactions between different regions (or segments) are appropriately captured, along with their region-wise features in their region adjacency graphs. Also, the attention mechanism has often been applied to the nodes to highlight the essential features in each node. In this regard, a significant amount of high-frequency information is missed since each image segment is effectively summarized within a single node. To account for this and increase the learning capacity, we propose to attend over the edge/adjacency matrix to highlight the interactions among meaningful regions that contribute to supervised learning from images. We exploit this novel edge attention mechanism together with node attention to highlight essential image context by allowing more importance to the meaningful neighboring regions that highlight a relevant node. We implement the proposed context-attended GCN framework for image retrieval on the benchmarked UC-Merced and the PatternNet datasets. We observe a notable improvement in the results compared to the state of the art.

Item URL in elib:https://elib.dlr.de/144951/
Document Type:Article
Title:Attention-Driven Graph Convolution Network for Remote Sensing Image Retrieval
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Chaudhuri, UshashiIndian Institute of Technology BombayUNSPECIFIED
Banerjee, BiplabIndian Institute of Technology BombayUNSPECIFIED
Bhattacharya, AvikIndian Institute of Technology BombayUNSPECIFIED
Datcu, MihaiMihai.Datcu (at) dlr.deUNSPECIFIED
Date:January 2022
Journal or Publication Title:IEEE Geoscience and Remote Sensing Letters
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In ISI Web of Science:Yes
DOI :10.1109/LGRS.2021.3105448
Page Range:p. 8019705
Publisher:IEEE - Institute of Electrical and Electronics Engineers
Series Name:IEEE Geoscience and Remote Sensing Letters
Keywords:Attention network, graph convolution networks (GCNs), image retrieval, remote sensing (RS), Siamese architecture
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Earth Observation
DLR - Research theme (Project):R - Artificial Intelligence
Location: Oberpfaffenhofen
Institutes and Institutions:Remote Sensing Technology Institute > EO Data Science
Deposited By: Otgonbaatar, Soronzonbold
Deposited On:02 Nov 2021 13:03
Last Modified:14 Jan 2022 14:26

Repository Staff Only: item control page

Help & Contact
electronic library is running on EPrints 3.3.12
Copyright © 2008-2017 German Aerospace Center (DLR). All rights reserved.