EFFECTIVE CLASSIFICATION OF LOCAL CLIMATE ZONES BASED ON MULTI-SOURCE REMOTE SENSING DATA

被引：0
|
作者：
Jing, Hao [1 ,2 ,3 ]
Feng, Yingchao [1 ,2 ,3 ]
Zhang, Wenkai [1 ,2 ]
Zhang, Yue [1 ,2 ]
Wang, Siyue [4 ]
Fu, Kun [1 ,2 ]
Chen, Kaiqiang [1 ,2 ,3 ]
机构：
[1] Chinese Acad Sci, Inst Elect, Beijing, Peoples R China
[2] Chinese Acad Sci, Inst Elect, Key Lab Network Informat Syst Technol NIST, Beijing, Peoples R China
[3] Univ Chinese Acad Sci, Beijing, Peoples R China
[4] Northeastern Univ, Dept Elect & Comp Engn, Boston, MA 02115 USA
来源：
2019 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS 2019) | 2019年
基金：
中国国家自然科学基金;
关键词：
Multi-modality; local climate zone (LCZ) classification; convolutional neural networks; SAR; multi-spectral imagery;
D O I：
10.1109/igarss.2019.8898475
中图分类号：
P [天文学、地球科学];
学科分类号：
07 ;
摘要：
The local climate zone (LCZ) classification divides the urban areas into 17 categories, which are composed of 10 man-made structures and 7 natural landscapes. Though originally designed for temperature study, LCZ classification can be used for studies on economy and population. In this paper, we achieve a LCZ classification with convolutional neural networks based on the multi-source remote sensing data, including the polarimetric synthetic aperture radar (PolSAR) data and the corresponding multi-spectral imagery (MSI). Through experiments we attempt to reveal the contributions of the SAR data and the MSI to the classification performance. Furthermore, we emphasize the crucial importance of the preprocessing on the training data to derive a balanced dataset. We are ranked second in the Tianchi competition rankings when we submit our results.
引用
收藏
页码：2666 / 2669
页数：4
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