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Open AccessArticle

Feature Importance Analysis for Local Climate Zone Classification Using a Residual Convolutional Neural Network with Multi-Source Datasets

1
Signal Processing in Earth Observation, Technical University of Munich (TUM), 80333 Munich, Germany
2
Helmholtz-Zentrum Dresden-Rossendorf (HZDR), Helmholtz Institute Freiberg for Resource Technology (HIF), Exploration, D-09599 Freiberg, Germany
3
Remote Sensing Technology Institute (IMF), German Aerospace Center (DLR), 82234 Wessling, Germany
*
Author to whom correspondence should be addressed.
Remote Sens. 2018, 10(10), 1572; https://doi.org/10.3390/rs10101572
Received: 2 August 2018 / Revised: 21 September 2018 / Accepted: 27 September 2018 / Published: 1 October 2018
(This article belongs to the Special Issue Multisensor Data Fusion in Remote Sensing)
Global Local Climate Zone (LCZ) maps, indicating urban structures and land use, are crucial for Urban Heat Island (UHI) studies and also as starting points to better understand the spatio-temporal dynamics of cities worldwide. However, reliable LCZ maps are not available on a global scale, hindering scientific progress across a range of disciplines that study the functionality of sustainable cities. As a first step towards large-scale LCZ mapping, this paper tries to provide guidance about data/feature choice. To this end, we evaluate the spectral reflectance and spectral indices of the globally available Sentinel-2 and Landsat-8 imagery, as well as the Global Urban Footprint (GUF) dataset, the OpenStreetMap layers buildings and land use and the Visible Infrared Imager Radiometer Suite (VIIRS)-based Nighttime Light (NTL) data, regarding their relevance for discriminating different Local Climate Zones (LCZs). Using a Residual convolutional neural Network (ResNet), a systematic analysis of feature importance is performed with a manually-labeled dataset containing nine cities located in Europe. Based on the investigation of the data and feature choice, we propose a framework to fully exploit the available datasets. The results show that GUF, OSM and NTL can contribute to the classification accuracy of some LCZs with relatively few samples, and it is suggested that Landsat-8 and Sentinel-2 spectral reflectances should be jointly used, for example in a majority voting manner, as proven by the improvement from the proposed framework, for large-scale LCZ mapping. View Full-Text
Keywords: Local Climate Zones (LCZs); Sentinel-2; Landsat-8; spectral reflectance; classification; Residual convolutional neural Network (ResNet) Local Climate Zones (LCZs); Sentinel-2; Landsat-8; spectral reflectance; classification; Residual convolutional neural Network (ResNet)
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Qiu, C.; Schmitt, M.; Mou, L.; Ghamisi, P.; Zhu, X.X. Feature Importance Analysis for Local Climate Zone Classification Using a Residual Convolutional Neural Network with Multi-Source Datasets. Remote Sens. 2018, 10, 1572.

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