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Detecting unknown artificial urban surface materials based on spectral dissimilarity analysis

Jilge, Marianne and Heiden, Uta and Habermeyer, Martin and Mende, Andre and Jürgens, Carsten (2017) Detecting unknown artificial urban surface materials based on spectral dissimilarity analysis. Sensors, 17 (1826), pp. 1-20. Multidisciplinary Digital Publishing Institute (MDPI). doi: 10.3390/s17081826. ISSN 1424-8220.

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Official URL: http://www.mdpi.com/si/sensors/monitoring_synthesis_modeling


High resolution imaging spectroscopy data have been recognised as a valuable data resource for augmenting detailed material inventories that serve as input for various urban applications. Image-specific urban spectral libraries are successfully used in urban imaging spectroscopy studies. However, the regional- and sensor-specific transferability of such libraries is limited due to the wide range of different surface materials. With the developed methodology, incomplete urban spectral libraries can be utilised by assuming that unknown surface material spectra are dissimilar to the known spectra in a basic spectral library (BSL). The similarity measure SID-SCA (Spectral Information Divergence-Spectral Correlation Angle) is applied to detect image-specific unknown urban surfaces while avoiding spectral mixtures. These detected unknown materials are categorised into distinct and identifiable material classes based on their spectral and spatial metrics. Experimental results demonstrate a successful redetection of material classes that had been previously erased in order to simulate an incomplete BSL. Additionally, completely new materials e.g., solar panels were identified in the data. It is further shown that the level of incompleteness of the BSL and the defined dissimilarity threshold are decisive for the detection of unknown material classes and the degree of spectral intra-class variability. A detailed accuracy assessment of the pre-classification results, aiming to separate natural and artificial materials, demonstrates spectral confusions between spectrally similar materials utilizing SID-SCA. However, most spectral confusions occur between natural or artificial materials which are not affecting the overall aim. The dissimilarity analysis overcomes the limitations of working with incomplete urban spectral libraries and enables the generation of image-specific training databases.

Item URL in elib:https://elib.dlr.de/114103/
Document Type:Article
Additional Information:This work was supported by the German Aerospace Center (DLR)—Project Management Agency and the Ministry of Economics and Technology (BMWi), Germany, as part of the EnFusionMAP project (50 EE 1343).
Title:Detecting unknown artificial urban surface materials based on spectral dissimilarity analysis
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Heiden, UtaUNSPECIFIEDhttps://orcid.org/0000-0002-3865-1912UNSPECIFIED
Habermeyer, MartinUNSPECIFIEDhttps://orcid.org/0009-0002-6364-2484UNSPECIFIED
Mende, Andrelandratsamt des landkreises zwickauUNSPECIFIEDUNSPECIFIED
Jürgens, Carstenuniversität bochumUNSPECIFIEDUNSPECIFIED
Date:8 August 2017
Journal or Publication Title:Sensors
Refereed publication:Yes
Open Access:Yes
Gold Open Access:Yes
In ISI Web of Science:Yes
Page Range:pp. 1-20
Publisher:Multidisciplinary Digital Publishing Institute (MDPI)
Keywords:imaging spectroscopy; urban areas; spectral library; dissimilarity; unknown surface materials
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:other
DLR - Research area:Raumfahrt
DLR - Program:R - no assignment
DLR - Research theme (Project):R - no assignment
Location: Oberpfaffenhofen
Institutes and Institutions:German Remote Sensing Data Center > Land Surface
Deposited By: Jilge, Marianne
Deposited On:25 Sep 2017 09:49
Last Modified:28 Mar 2023 23:49

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