TY - JOUR
T1 - Development of S-NPP VIIRS global surface type classification map using support vector machines
AU - Zhang, Rui
AU - Huang, Chengquan
AU - Zhan, Xiwu
AU - Jin, Huiran
AU - Song, Xiao Peng
N1 - Publisher Copyright:
© 2017 Informa UK Limited, trading as Taylor & Francis Group.
Copyright:
Copyright 2017 Elsevier B.V., All rights reserved.
PY - 2018/2/1
Y1 - 2018/2/1
N2 - With the launch of the Joint Polar Satellite System (JPSS)/Suomi National Polar-orbiting Partnership (S-NPP) satellite in October 2011, many of the terrestrial remote sensing products generated from Moderate Resolution Imaging Spectroradiometer (MODIS), such as the global land cover map, have been inherited and expanded into the JPSS/S-NPP mission using the new Visible Infrared Imaging Radiometer Suite (VIIRS) data. In this study, an improved algorithm including the use of a new classifier support vector machines (SVM) classifier was proposed to produce VIIRS surface type maps. In addition to the new classification algorithm, a new post-processing strategy involving the use of new ancillary data to refine the classification output is implemented. As a result, the new global International Geosphere-Biosphere Programme (IGBP) map based on the 2014 VIIRS surface reflectance data was generated with a 78.5 ± 0.6% overall classification accuracy. The new map was compared to a previously delivered VIIRS surface type map, and to the MODIS land cover product. Validation results including the error matrix, overall accuracy, and the user’s and producer’s accuracy suggest the new global surface type map provides similar classification accuracy compared to the old VIIRS surface type map, with higher accuracy achieved in agricultural types.
AB - With the launch of the Joint Polar Satellite System (JPSS)/Suomi National Polar-orbiting Partnership (S-NPP) satellite in October 2011, many of the terrestrial remote sensing products generated from Moderate Resolution Imaging Spectroradiometer (MODIS), such as the global land cover map, have been inherited and expanded into the JPSS/S-NPP mission using the new Visible Infrared Imaging Radiometer Suite (VIIRS) data. In this study, an improved algorithm including the use of a new classifier support vector machines (SVM) classifier was proposed to produce VIIRS surface type maps. In addition to the new classification algorithm, a new post-processing strategy involving the use of new ancillary data to refine the classification output is implemented. As a result, the new global International Geosphere-Biosphere Programme (IGBP) map based on the 2014 VIIRS surface reflectance data was generated with a 78.5 ± 0.6% overall classification accuracy. The new map was compared to a previously delivered VIIRS surface type map, and to the MODIS land cover product. Validation results including the error matrix, overall accuracy, and the user’s and producer’s accuracy suggest the new global surface type map provides similar classification accuracy compared to the old VIIRS surface type map, with higher accuracy achieved in agricultural types.
KW - Global surface type
KW - SVM
KW - VIIRS
KW - land cover
UR - http://www.scopus.com/inward/record.url?scp=85017413680&partnerID=8YFLogxK
U2 - 10.1080/17538947.2017.1315462
DO - 10.1080/17538947.2017.1315462
M3 - Article
AN - SCOPUS:85017413680
VL - 11
SP - 212
EP - 232
JO - International Journal of Digital Earth
JF - International Journal of Digital Earth
SN - 1753-8947
IS - 2
ER -