WEAKLY SUPERVISED SEMANTIC SEGMENTATION OF SATELLITE IMAGES FOR LAND COVER MAPPING – CHALLENGES AND OPPORTUNITIES

WEAKLY SUPERVISED SEMANTIC SEGMENTATION OF SATELLITE IMAGES FOR LAND COVER MAPPING – CHALLENGES AND OPPORTUNITIES

Fully automatic large-scale land cover mapping belongs to the core challenges addressed by the remote sensing community. Usually, the basis of this task is formed by (supervised) machine learning models. However, in spite of recent growth in the availability of satellite observations, accurate train...

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Bibliographic Details
Main Authors:	M. Schmitt, J. Prexl, P. Ebel, L. Liebel, X. X. Zhu
Format:	Article
Language:	English
Published:	Copernicus Publications 2020-08-01
Series:	ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Online Access:	https://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/V-3-2020/795/2020/isprs-annals-V-3-2020-795-2020.pdf

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