TY - GEN
T1 - On the Fusion Strategies of Sentinel-1 and Sentinel-2 Data for Local Climate Zone Classification
AU - Gawlikowski, Jakob
AU - Schmitt, Michael
AU - Kruspe, Anna
AU - Zhu, Xiao Xiang
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/9/26
Y1 - 2020/9/26
N2 - Local Climate Zone (LCZ) classification is the most commonly used scheme to analyze how local urban morphology affects the climate of local areas. Classification methods are often based on remote sensing data or on a fusion of several data sources. In this study, the effects of different fusion strategies of optical and synthetic aperture radar (SAR) data on the accuracy of LCZ classifications are investigated. The data processing is implemented with a convolutional neural network (CNN), where until a fusion layer, separate data sources are processed separately on branches. Strategies of splitting the data into branches and the effects of different fusion stages are compared, together with approaches based on sums of independent classifiers. For our setting, the stage of fusion does not seem to have a big influence on the accuracy. The results of this study contribute to a better understanding of cooperative usage of multispectral and SAR data.
AB - Local Climate Zone (LCZ) classification is the most commonly used scheme to analyze how local urban morphology affects the climate of local areas. Classification methods are often based on remote sensing data or on a fusion of several data sources. In this study, the effects of different fusion strategies of optical and synthetic aperture radar (SAR) data on the accuracy of LCZ classifications are investigated. The data processing is implemented with a convolutional neural network (CNN), where until a fusion layer, separate data sources are processed separately on branches. Strategies of splitting the data into branches and the effects of different fusion stages are compared, together with approaches based on sums of independent classifiers. For our setting, the stage of fusion does not seem to have a big influence on the accuracy. The results of this study contribute to a better understanding of cooperative usage of multispectral and SAR data.
KW - Data Fusion
KW - Fusion Network
KW - Local Climate Zone Classification
UR - http://www.scopus.com/inward/record.url?scp=85090327381&partnerID=8YFLogxK
U2 - 10.1109/IGARSS39084.2020.9324234
DO - 10.1109/IGARSS39084.2020.9324234
M3 - Conference contribution
AN - SCOPUS:85090327381
T3 - International Geoscience and Remote Sensing Symposium (IGARSS)
SP - 2081
EP - 2084
BT - 2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020
Y2 - 26 September 2020 through 2 October 2020
ER -