Conference Proceeding

Monitoring Aquatic Weeds In Indian Wetlands Using Multitemporal Remote Sensing Data With Machine Learning Techniques

Details

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. https://doi.org/10.1109/IGARSS47720.2021.9553207

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.

Keywords
Remote sensing; multitemporal image analysis; Sentinel-1; Sentinel-2; water hyacinth; Eichhornia crassipes; wetland; machine learning

Status	Published
Funders	Royal Academy of Engineering
Publication date	31/12/2021
Publication date online	12/10/2021
URL	http://hdl.handle.net/1893/32852
Publisher	IEEE
Place of publication	Piscataway, NJ, USA
ISSN of series	2153-7003
eISBN	978-1-6654-0369-6
Conference	International Geoscience and Remote Sensing Symposium (IGARSS)
Conference location	Belgium
Dates	12/07/2021–16/07/2021