NDLI: Detecting Unknown Artificial Urban Surface Materials Based on Spectral Dissimilarity Analysis

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Detecting Unknown Artificial Urban Surface Materials Based on Spectral Dissimilarity Analysis

Content Provider	MDPI
Author	Jilge, Marianne Heiden, Uta Habermeyer, Martin Mende, André Juergens, Carsten
Abstract	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.
File Size	2951168
File Format	PDF
e-ISSN	14248220
DOI	10.3390/s17081826
Journal	Sensors
Issue Number	8
Volume Number	17
Language	English
Publisher Date	2017-08-08
Access Restriction	Open
Subject Keyword	imaging spectroscopy urban areas spectral library dissimilarity unknown surface materials
Content Type	Text
Resource Type	Article

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