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Sampling Robustness in Gradient Analysis of Urban Material Mixtures

Ji, Chaonan and Jilge, Marianne and Heiden, Uta and Stellmes, Marion and Feilhauer, Hannes (2022) Sampling Robustness in Gradient Analysis of Urban Material Mixtures. IEEE Transactions on Geoscience and Remote Sensing, 60, p. 4500211. IEEE - Institute of Electrical and Electronics Engineers. doi: 10.1109/TGRS.2020.3040342. ISSN 0196-2892.

[img] PDF - Postprint version (accepted manuscript)

Official URL: https://ieeexplore.ieee.org/abstract/document/9285190


Many studies analyzing spaceborne hyperspectral images (HSIs) have so far struggled to deal with a lack of pure pixels due to complex mixtures of urban surface materials. Recently, an alternative concept of gradients in urban surface material composition has been proposed and successfully applied to map cities with spaceborne HSIs without the requirement for a previous determination of pure pixels. The gradient concept treats all pixels as mixed and aims to describe and quantify gradual transitions in the cover fractions of surface materials. This concept presents a promising approach to tackle urban mapping using spaceborne HSIs. However, since gradients are determined in a data-driven way, their transferability within urban areas needs to be investigated. For this purpose, we analyze the robustness of urban surface material gradients and their dependence across six systematic and three simple random sampling schemes. The results show high similarity between nine sampling schemes in the primary gradient feature space (Pspace) and individual gradient feature spaces (Ispaces). Comparing the Pspace with the Ispaces, the Mantel statistics show the resemblance of samples' distribution in the Pspace, and each Ispace is rather strong with high credibility, as the significance level is P < 0.01. Therefore, it can be concluded that the material gradients defined in the test area are independent of the specific sampling scheme. This study paves the way for subsequent analysis of the stability of urban surface material gradients and the interpretation of material gradients in other urban environments.

Item URL in elib:https://elib.dlr.de/142236/
Document Type:Article
Title:Sampling Robustness in Gradient Analysis of Urban Material Mixtures
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Ji, ChaonanUNSPECIFIEDhttps://orcid.org/0000-0001-8154-0508
Heiden, UtaUNSPECIFIEDhttps://orcid.org/0000-0002-3865-1912
Stellmes, MarionUNSPECIFIEDhttps://orcid.org/0000-0001-7325-6152
Feilhauer, HannesUNSPECIFIEDhttps://orcid.org/0000-0001-5758-6303
Date:January 2022
Journal or Publication Title:IEEE Transactions on Geoscience and Remote Sensing
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In ISI Web of Science:Yes
Page Range:p. 4500211
Publisher:IEEE - Institute of Electrical and Electronics Engineers
Keywords:Gradient analysis, hyperspectral images (HSIs), sampling robustness, transferability,urban mapping
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Earth Observation
DLR - Research theme (Project):R - Remote Sensing and Geo Research, R - Optical remote sensing
Location: Oberpfaffenhofen
Institutes and Institutions:German Remote Sensing Data Center > Land Surface Dynamics
Remote Sensing Technology Institute > Photogrammetry and Image Analysis
Deposited By: Ji, Chaonan
Deposited On:25 May 2021 09:22
Last Modified:01 Mar 2023 03:00

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