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Monitoring Aquatic Weeds in Indian Wetlands Using Multitemporal Remote Sensing Data with Machine Learning Techniques

Lookup NU author(s): Dr Deepayan BhowmikORCiD


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© 2021 IEEE. The main objective of this paper to show the potential of multitemporal Sentinel-1 (S-1) and Sentinel-2 (S-2) for detection of water hyacinth in Indian wetlands. Water hyacinth (Pontederia crassipes, also called Eichhornia crassipes) is one of the most destructive invasive weed species in many lakes and river systems worldwide, causing significant adverse economic and ecological impacts. We use the expectation maximization (EM) as a benchmark machine learning algorithm and compare its results with three supervised machine learning classifiers, Support Vector Machine (SVM), Random Forest (RF), and k-Nearest Neighbour (kNN), using both synthetic aperture radar (SAR) and optical data to distinguish between clean and infested waters.

Publication metadata

Author(s): Akbari V, Simpson M, Maharaj S, Marino A, Bhowmik D, Prabhu GN, Rupavatharam S, Datta A, Kleczkowski A, Sujeetha JRPA

Publication type: Conference Proceedings (inc. Abstract)

Publication status: Published

Conference Name: International Geoscience and Remote Sensing Symposium (IGARSS 2021)

Year of Conference: 2021

Pages: 6847-6850

Online publication date: 12/10/2021

Acceptance date: 02/04/2018

ISSN: 2153-7003

Publisher: IEEE


DOI: 10.1109/IGARSS47720.2021.9553207

Library holdings: Search Newcastle University Library for this item

ISBN: 9781665403696