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Appears in Collections:Computing Science and Mathematics Conference Papers and Proceedings
Author(s): Akbari, Vahid
Simpson, Morgan
Maharaj, Savitri
Marino, Armando
Bhowmik, Deepayan
Prabhu, G Nagendra
Rupavatharam, Srikanth
Datta, Aviraj
Kleczkowski, Adam
Sujeetha, J Alice R P
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Title: Monitoring Aquatic Weeds In Indian Wetlands Using Multitemporal Remote Sensing Data With Machine Learning Techniques
Citation: Akbari V, Simpson M, Maharaj S, Marino A, Bhowmik D, Prabhu GN, Rupavatharam S, Datta A, Kleczkowski A & Sujeetha JARP (2021) Monitoring Aquatic Weeds In Indian Wetlands Using Multitemporal Remote Sensing Data With Machine Learning Techniques. In: 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS. International Geoscience and Remote Sensing Symposium (IGARSS), Belgium, 12.07.2021-16.07.2021. Piscataway, NJ, USA: IEEE.
Issue Date: 2021
Date Deposited: 3-Jul-2021
Conference Name: International Geoscience and Remote Sensing Symposium (IGARSS)
Conference Dates: 2021-07-12 - 2021-07-16
Conference Location: Belgium
Abstract: The main objective of this paper to show the potential of mul-titemporal 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.
Status: AM - Accepted Manuscript
Rights: © 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.

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