Monitoring aquatic weeds in Indian wetlands using multitemporal remote sensing data with machine learning techniques

Akbari, Vahid and Simpson, Morgan and Maharaj, Savitri and Marino, Armando and Bhowmik, Deepayan and Prabhu, G. Nagendra and Rupavatharam, Srikanth and Datta, Aviraj and Kleczkowski, Adam and Sujeetha, J. R.P.Alice; (2021) Monitoring aquatic weeds in Indian wetlands using multitemporal remote sensing data with machine learning techniques. In: IGARSS 2021 - 2021 IEEE International Geoscience and Remote Sensing Symposium, Proceedings. International Geoscience and Remote Sensing Symposium (IGARSS) . Institute of Electrical and Electronics Engineers Inc., BEL, pp. 6847-6850. ISBN 9781665403696 (https://doi.org/10.1109/IGARSS47720.2021.9553207)

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Abstract

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.

  • Item type:Book Section
    ID code:80008
    Dates:
    Date
    Event
    12 October 2021
    Published
    16 July 2021
    Published Online
    8 April 2021
    Accepted
    Notes:© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
    Subjects:Science > Mathematics > Electronic computers. Computer science
    Department:Faculty of Science > Mathematics and Statistics
    Depositing user: Pure Administrator
    Date deposited:30 Mar 2022 15:54
    Last modified:18 Apr 2024 00:08
    Related URLs:
    URI:https://strathprints.strath.ac.uk/id/eprint/80008
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