TY - GEN
T1 - Feature importance analysis of sentinel-2 imagery for large-scale urban local climate zone classification
AU - Qiu, Chunping
AU - Schmitt, Michael
AU - Ghamisi, Pedram
AU - Mou, Lichao
AU - Zhu, Xiao Xiang
N1 - Publisher Copyright:
© 2018 IEEE
PY - 2018/10/31
Y1 - 2018/10/31
N2 - This paper evaluates different spectral-spatial features that can be extracted from Sentinel-2 imagery regarding their relevance for discriminating different Local Climate Zone (LCZ) classes. The features include spectral reflectance, spectral indices, Morphological Profiles (MPs), as well as Global Urban Footprint (GUF), the Open Street Map layers buildings and land use, and their combinations. Using a residual convolutional neural network (ResNet), a systematic analysis of feature importance is performed with a manually generated dataset distributed in Europe. The results of this evaluation are meant to provide guidance about the choice of both spectral and spatial features for the task of LCZ classification on a global scale. The results show that GUF and OSM can contribute to the classification performance, and ResNet relies less on additional features with the highest accuracy provided by the reflectance only.
AB - This paper evaluates different spectral-spatial features that can be extracted from Sentinel-2 imagery regarding their relevance for discriminating different Local Climate Zone (LCZ) classes. The features include spectral reflectance, spectral indices, Morphological Profiles (MPs), as well as Global Urban Footprint (GUF), the Open Street Map layers buildings and land use, and their combinations. Using a residual convolutional neural network (ResNet), a systematic analysis of feature importance is performed with a manually generated dataset distributed in Europe. The results of this evaluation are meant to provide guidance about the choice of both spectral and spatial features for the task of LCZ classification on a global scale. The results show that GUF and OSM can contribute to the classification performance, and ResNet relies less on additional features with the highest accuracy provided by the reflectance only.
KW - Classification
KW - Local Climate Zones (LCZs)
KW - Morphological Profiles (MPs)
KW - Residual convolutional neural network (ResNet)
KW - Sentinel-2
KW - Spectral features
UR - http://www.scopus.com/inward/record.url?scp=85063136884&partnerID=8YFLogxK
U2 - 10.1109/IGARSS.2018.8517732
DO - 10.1109/IGARSS.2018.8517732
M3 - Conference contribution
AN - SCOPUS:85063136884
T3 - International Geoscience and Remote Sensing Symposium (IGARSS)
SP - 4681
EP - 4684
BT - 2018 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 38th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018
Y2 - 22 July 2018 through 27 July 2018
ER -
