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FRACTIONAL SNOW COVER MAPPING BY ARTIFICIAL NEURAL NETWORKS AND SUPPORT VECTOR MACHINES
2017
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Snow is an important land cover whose distribution over space and time plays a significant role in various environmental processes. Hence, snow cover mapping with high accuracy is necessary to have a real understanding for present and future climate, water cycle, and ecological changes. This study aims to investigate and compare the design and use of artificial neural networks (ANNs) and support vector machines (SVMs) algorithms for fractional snow cover (FSC) mapping from satellite data. ANN
doi:10.5194/isprs-annals-iv-4-w4-179-2017
fatcat:shrogmhkavbkto77zltq33amoq