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. 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

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