Urban Local Climate Zone Classification with A Residual Convolutional Neural Network and Multi-Seasonal Sentinel-2 Images

被引：0

作者：

Qiu Chunping ^{[1
]}

Mou Lichao ^{[1
]}

Schmitt, Michael ^{[1
]}

Zhu Xiaoxiang ^{[1
,2
]}

机构：

[1] TUM, Signal Proc Earth Observat, Munich, Germany

[2] German Aerosp Ctr DLR, Remote Sensing Technol Inst IMF, Wessling, Germany

来源：

2018 10TH IAPR WORKSHOP ON PATTERN RECOGNITION IN REMOTE SENSING (PRRS) | 2018年

基金：

欧洲研究理事会;

关键词：

Local climate zones (LCZs) oIoI Sentinel-2; spectral features; classification; multi-seasonal; Residual Convolutional Neural Network (ResNet);

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This study presents a classification framework for the urban Local Climate Zones (LCZs) based on a Residual Convolutional Neural Network (ResNet) architecture. In order to make full use of the temporal and spectral information contained in modern Earth observation data, multi-seasonal Sentinel-2 images are exploited. After training the ResNet, independent predictions are made from the multi-seasonal images. Subsequently, the seasonal predictions are fused in a decision fusion step based on majority voting. A systematical experiment is carried out in a large-scale study area located in the center of Europe. A significant accuracy improvement can be achieved by applying majority voting on multi-seasonal predictions. Based on the results, the main challenges and possible solutions of urban LCZ classification are further discussed, providing guidance for large-scale urban LCZ mapping.

引用

页数：5