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

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

doi:10.1109/IGARSS47720.2021.9553207

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

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

Mathematics And Statistics

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution book

13 Citations (Scopus)

54 Downloads (Pure)

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.

Original language	English
Title of host publication	IGARSS 2021 - 2021 IEEE International Geoscience and Remote Sensing Symposium, Proceedings
Place of Publication	New York, N.Y.
Pages	6847-6850
Number of pages	4
ISBN (Electronic)	9781665403696
DOIs	https://doi.org/10.1109/IGARSS47720.2021.9553207
Publication status	Published - 12 Oct 2021
Event	2021 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2021 - Brussels, Belgium Duration: 12 Jul 2021 → 16 Jul 2021

Publication series

Name	International Geoscience and Remote Sensing Symposium (IGARSS)
Volume	2021-July

Conference

Conference	2021 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2021
Country/Territory	Belgium
City	Brussels
Period	12/07/21 → 16/07/21

Funding

Acknowledgement: This work was funded by the Royal Academy of Engineering under the Frontiers Follow-on Funding scheme (FF9207)

Keywords

Eichhornia crassipes
machine learning
multitemporal image analysis
remote sensing
Sentinel-1
Sentinel-2
water hyacinth
wetland

Access to Document

10.1109/IGARSS47720.2021.9553207

Akbari-etal-IGARSS-2021-Monitoring-aquatic-weeds-in-Indian-wetlands-using-multitemporal-remote-sensing-dataAccepted author manuscript, 9.62 MBLicence: Strath_1

Cite this

Akbari, V., Simpson, M., Maharaj, S., Marino, A., Bhowmik, D., Prabhu, G. N., Rupavatharam, S., Datta, A., Kleczkowski, A., & Sujeetha, J. R. P. A. (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 (pp. 6847-6850). (International Geoscience and Remote Sensing Symposium (IGARSS); Vol. 2021-July).. https://doi.org/10.1109/IGARSS47720.2021.9553207

Akbari, Vahid ; Simpson, Morgan ; Maharaj, Savitri et al. / Monitoring aquatic weeds in Indian wetlands using multitemporal remote sensing data with machine learning techniques. IGARSS 2021 - 2021 IEEE International Geoscience and Remote Sensing Symposium, Proceedings. New York, N.Y., 2021. pp. 6847-6850 (International Geoscience and Remote Sensing Symposium (IGARSS)).

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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.",

keywords = "Eichhornia crassipes, machine learning, multitemporal image analysis, remote sensing, Sentinel-1, Sentinel-2, water hyacinth, wetland",

author = "Vahid Akbari and Morgan Simpson and Savitri Maharaj and Armando Marino and Deepayan Bhowmik and Prabhu, {G. Nagendra} and Srikanth Rupavatharam and Aviraj Datta and Adam Kleczkowski and Sujeetha, {J. R.P.Alice}",

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Akbari, V, Simpson, M, Maharaj, S, Marino, A, Bhowmik, D, Prabhu, GN, Rupavatharam, S, Datta, A, Kleczkowski, A & Sujeetha, JRPA 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), vol. 2021-July, New York, N.Y., pp. 6847-6850, 2021 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2021, Brussels, Belgium, 12/07/21. https://doi.org/10.1109/IGARSS47720.2021.9553207

Monitoring aquatic weeds in Indian wetlands using multitemporal remote sensing data with machine learning techniques. / Akbari, Vahid; Simpson, Morgan; Maharaj, Savitri et al.
IGARSS 2021 - 2021 IEEE International Geoscience and Remote Sensing Symposium, Proceedings. New York, N.Y., 2021. p. 6847-6850 (International Geoscience and Remote Sensing Symposium (IGARSS); Vol. 2021-July).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution book

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AU - Akbari, Vahid

AU - Simpson, Morgan

AU - Maharaj, Savitri

AU - Marino, Armando

AU - Bhowmik, Deepayan

AU - Prabhu, G. Nagendra

AU - Rupavatharam, Srikanth

AU - Datta, Aviraj

AU - Kleczkowski, Adam

AU - Sujeetha, J. R.P.Alice

N1 - © 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.

PY - 2021/10/12

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N2 - 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.

AB - 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.

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M3 - Conference contribution book

AN - SCOPUS:85124348417

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Akbari V, Simpson M, Maharaj S, Marino A, Bhowmik D, Prabhu GN et al. 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. New York, N.Y. 2021. p. 6847-6850. (International Geoscience and Remote Sensing Symposium (IGARSS)). Epub 2021 Jul 16. doi: 10.1109/IGARSS47720.2021.9553207

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

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