Exploring Subpixel Learning Algorithms for Estimating Global Land Cover Fractions from Satellite Data Using High Performance Computing

Exploring Subpixel Learning Algorithms for Estimating Global Land Cover Fractions from Satellite Data Using High Performance Computing

Land cover (LC) refers to the physical and biological cover present over the Earth’s surface in terms of the natural environment such as vegetation, water, bare soil, etc. Most LC features occur at finer spatial scales compared to the resolution of primary remote sensing satellites. Therefore, obser...

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Bibliographic Details
Main Authors: Uttam Kumar, Sangram Ganguly, Ramakrishna R. Nemani, Kumar S Raja, Cristina Milesi, Ruchita Sinha, Andrew Michaelis, Petr Votava, Hirofumi Hashimoto, Shuang Li, Weile Wang, Subodh Kalia, Shreekant Gayaka
Format: Article
Language:English
Published: MDPI AG 2017-10-01
Series:Remote Sensing
Subjects:
machine learning
subpixel classification
Landsat
WELD
mixed pixel
global endmembers
land cover
high performance computing
nighttime lights
Online Access:https://www.mdpi.com/2072-4292/9/11/1105

Internet

https://www.mdpi.com/2072-4292/9/11/1105

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