Details

Autor(en) / Beteiligte

• Akbari, Vahid
• Simpson, Morgan
• Maharaj, Savitri
• Marino, Armando
• Bhowmik, Deepayan
• Prabhu, G. Nagendra
• Rupavatharam, Srikanth
• Datta, Aviraj
• Kleczkowski, Adam
• Sujeetha, J. Alice R. P.

Titel

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

Ist Teil von

• 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, 2021, p.6847-6850

Ort / Verlag

IEEE

Erscheinungsjahr

2021

Link zum Volltext

• IEEE_Xplore

Quelle

IEEE Electronic Library (IEL)

Beschreibungen/Notizen

• 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.