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Feature Extraction and Selection of Sentinel-1 Dual-Pol Data for Global-Scale Local Climate Zone Classification

Hu, Jingliang and Ghamisi, Pedram and Zhu, Xiao Xiang (2018) Feature Extraction and Selection of Sentinel-1 Dual-Pol Data for Global-Scale Local Climate Zone Classification. ISPRS International Journal of Geo-Information, 7 (379), pp. 1-20. Multidisciplinary Digital Publishing Institute (MDPI). DOI: 10.3390/ijgi7090379 ISSN 2220-9964

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Official URL: https://www.mdpi.com/2220-9964/7/9/379/pdf

Abstract

The concept of the local climate zone (LCZ) has been recently proposed as a generic land-cover/land-use classification scheme. It divides urban regions into 17 categories based on compositions of man-made structures and natural landscapes. Although it was originally designed for temperature study, the morphological structure concealed in LCZs also reflects economic status and population distribution. To this end, global LCZ classification is of great value for worldwide studies on economy and population. Conventional classification approaches are usually successful for an individual city using optical remote sensing data. This paper, however, attempts for the first time to produce global LCZ classification maps using polarimetric synthetic aperture radar (PolSAR) data. Specifically, we first produce polarimetric features, local statistical features, texture features, and morphological features and compare them, with respect to their classification performance. Here, an ensemble classifier is investigated, which is trained and tested on already separated transcontinental cities. Considering the challenging global scope this work handles, we conclude the classification accuracy is not yet satisfactory. However, Sentinel-1 dual-Pol SAR data could contribute the classification for several LCZ classes. According to our feature studies, the combination of local statistical features and morphological features yields the best classification results with 61.8% overall accuracy (OA), which is 3% higher than the OA produced by the second best features combination. The 3% is considerably large for a global scale. Based on our feature importance analysis, features related to VH polarized data contributed the most to the eventual classification result.

Item URL in elib:https://elib.dlr.de/123039/
Document Type:Article
Title:Feature Extraction and Selection of Sentinel-1 Dual-Pol Data for Global-Scale Local Climate Zone Classification
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Hu, Jingliangjingliang.hu (at) dlr.deUNSPECIFIED
Ghamisi, PedramMF-DASUNSPECIFIED
Zhu, Xiao Xiangxiao.zhu (at) dlr.deUNSPECIFIED
Date:18 September 2018
Journal or Publication Title:ISPRS International Journal of Geo-Information
Refereed publication:Yes
Open Access:Yes
Gold Open Access:Yes
In SCOPUS:Yes
In ISI Web of Science:Yes
Volume:7
DOI :10.3390/ijgi7090379
Page Range:pp. 1-20
Publisher:Multidisciplinary Digital Publishing Institute (MDPI)
ISSN:2220-9964
Status:Published
Keywords:Sentinel-1 dual-Pol data; local climate zone; global scale; feature extraction; GLCM; morphological profile; canonical correlation forest
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Erdbeobachtung
DLR - Research theme (Project):R - Vorhaben hochauflösende Fernerkundungsverfahren
Location: Oberpfaffenhofen
Institutes and Institutions:Remote Sensing Technology Institute > EO Data Science
Deposited By: Hu, Jingliang
Deposited On:15 Nov 2018 11:41
Last Modified:14 Dec 2019 04:22

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