Application of Spectral-Spatial Classification on Hyperspectral Imagery to Map Coastal Endmembers in Manicouagan Peninsula

Soham Mukherjee; Yanqun Pan; Gabriela Ifimov; Raymond Soffer; Simon Bélanger; Simon Bélanger

doi:10.1364/HISE.2021.HTu2C.4

OSA Optical Sensors and Sensing Congress 2021 (AIS, FTS, HISE, SENSORS, ES)
OSA Technical Digest (Optica Publishing Group, 2021),
paper HTu2C.4
•https://doi.org/10.1364/HISE.2021.HTu2C.4

Application of Spectral-Spatial Classification on Hyperspectral Imagery to Map Coastal Endmembers in Manicouagan Peninsula

Soham Mukherjee, Yanqun Pan, Gabriela Ifimov, Raymond Soffer, and Simon Bélanger

Hyperspectral Imaging and Sounding of the Environment 2021

Washington, DC United States
19–23 July 2021
ISBN: 978-1-943580-90-3

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Aquatic Remote Sensing I (HTu2C)

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S. Mukherjee, Y. Pan, G. Ifimov, R. Soffer, and S. Bélanger, "Application of Spectral-Spatial Classification on Hyperspectral Imagery to Map Coastal Endmembers in Manicouagan Peninsula," in OSA Optical Sensors and Sensing Congress 2021 (AIS, FTS, HISE, SENSORS, ES), S. Buckley, F. Vanier, S. Shi, K. Walker, I. Coddington, S. Paine, K. Lok Chan, W. Moses, S. Qian, P. Pellegrino, F. Vollmer, G. , J. Jágerská, R. Menzies, L. Emmenegger, and J. Westberg, eds., OSA Technical Digest (Optica Publishing Group, 2021), paper HTu2C.4.

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Abstract

The study aims to classify the coastline of Manicouagan Peninsula, QC using WaterSat Imaging Experiment (WISE) hyperspectral imagery. A probabilistic pixel-based spectral classification scheme is integrated with contextual spatial pattern regularization using Bayesian MAP approach.

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