Enhancing performance of multi-temporal tropical river landform classification through downscaling approaches

Li, Qing, Barrett, Brian, Williams, Richard, Hoey, Trevor and Boothroyd, Richard ORCID: 0000-0001-9742-4229 (2022) Enhancing performance of multi-temporal tropical river landform classification through downscaling approaches INTERNATIONAL JOURNAL OF REMOTE SENSING, 43 (17). pp. 6445-6462. ISSN 0143-1161, 1366-5901

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Abstract

Multi-temporal remote sensing imagery has the potential to classify river landforms to reconstruct the evolutionary trajectory of river morphologies. Whilst open-access archives of high spatial resolution imagery are increasingly available from satellite sensors, such as Sentinel-2, there remains a fundamental challenge of maximising the utility of information in each band whilst maintaining a sufficiently fine resolution to identify landforms. Although image fusion and downscaling methods on Sentinel-2 imagery have been investigated for many years, there is a need to assess their performance for multi-temporal object-based river landform classification. This investigation first compared three downscaling methods: area to point regression kriging (ATPRK), super-resolution based on Sen2Res, and nearest neighbour resampling. We assessed performance of the three downscaling methods by accuracy, precision, recall and F1-score. ATPRK was the optimal downscaling approach, achieving an overall accuracy of 0.861. We successively engaged a set of experiments to determine an optimal training model, exploring single and multi-date scenarios. We find that not only does remote sensing imagery with better quality improve river landform classification performance, but multi-date datasets for establishing machine learning models should be considered for contributing higher classification accuracy. This paper presents a workflow for automated river landform recognition that could be applied to other tropical rivers with similar hydro-geomorphological characteristics.

Item Type:	Article
Uncontrolled Keywords:	multi-temporal classification, image downscaling, river landforms, landform classification
Divisions:	Faculty of Science & Engineering > School of Environmental Sciences
Depositing User:	Symplectic Admin
Date Deposited:	07 Aug 2023 07:41
Last Modified:	16 Jun 2026 15:40
DOI:	10.1080/01431161.2022.2139164
Open Access URL:	https://doi.org/10.1080/01431161.2022.2139164
Related Websites:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3172043
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