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

Li, Q., Barrett, B. , Williams, R. , Hoey, T. and Boothroyd, R. (2022) Enhancing performance of multi-temporal tropical river landform classification through downscaling approaches. International Journal of Remote Sensing, 43(17), pp. 6445-6462. (doi: 10.1080/01431161.2022.2139164)

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: Articles
• Additional Information: The work was supported by the China Scholarship Council [201908060049] and University of Glasgow [201908060049].
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Boothroyd, Dr Richard and Williams, Professor Richard and Hoey, Professor Trevor and Barrett, Dr Brian and Li, Qing
• Authors: Li, Q., Barrett, B., Williams, R., Hoey, T., and Boothroyd, R.
• College/School: College of Science and Engineering; College of Science and Engineering > School of Geographical and Earth Sciences
• Journal Name: International Journal of Remote Sensing
• Publisher: Taylor & Francis
• ISSN: 0143-1161
• ISSN (Online): 1366-5901
• Published Online: 17 November 2022
• Copyright Holders: Copyright © 2022 The Authors
• First Published: First published in International Journal of Remote Sensing 43(17):6445-6462
• Publisher Policy: Reproduced under a Creative Commons License
• Data DOI: 10.5525/gla.researchdata.1355