AMM-FuseNet: attention-based multi-modal image fusion network for land cover mapping

Ma, Wanli, Karakuş, Oktay ORCID: https://orcid.org/0000-0001-8009-9319 and Rosin, Paul L. ORCID: https://orcid.org/0000-0002-4965-3884 2022. AMM-FuseNet: attention-based multi-modal image fusion network for land cover mapping. Remote Sensing 14 (18) , 4458. 10.3390/rs14184458

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Abstract

Land cover mapping provides spatial information on the physical properties of the Earth’s surface for various classes of wetlands, artificial surface and constructions, vineyards, water bodies, etc. Having reliable information on land cover is crucial to developing solutions to a variety of environmental problems, such as the destruction of important wetlands/forests, and loss of fish and wildlife habitats. This has made land cover mapping become one of the most widespread applications in remote sensing computational imaging. However, due to the differences between modalities in terms of resolutions, content, and sensors, integrating complementary information that multi-modal remote sensing imagery exhibits into a robust and accurate system still remains challenging, and classical segmentation approaches generally do not give satisfactory results for land cover mapping. In this paper, we propose a novel dynamic deep network architecture, AMM-FuseNet that promotes the use of multi-modal remote sensing images for the purpose of land cover mapping. The proposed network exploits the hybrid approach of the channel attention mechanism and densely connected atrous spatial pyramid pooling (DenseASPP). In the experimental analysis, in order to verify the validity of the proposed method, we test AMM-FuseNet with three datasets whilst comparing it to the six state-of-the-art models of DeepLabV3+, PSPNet, UNet, SegNet, DenseASPP, and DANet. In addition, we demonstrate the capability of AMM-FuseNet under minimal training supervision (reduced number of training samples) compared to the state of the art, achieving less accuracy loss, even for the case with 1/20 of the training samples.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Computer Science & Informatics
Additional Information:	License information from Publisher: LICENSE 1: URL: https://creativecommons.org/licenses/by/4.0/, Type: open-access
Publisher:	MDPI
Date of First Compliant Deposit:	10 October 2022
Date of Acceptance:	5 September 2022
Last Modified:	21 May 2023 22:22
URI:	https://orca.cardiff.ac.uk/id/eprint/153285

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