KLUM: An Urban VNIR and SWIR Spectral Library Consisting of Building Materials

Knowledge about the existing materials in urban areas has, in recent times, increased in importance. With the use of imaging spectroscopy and hyperspectral remote sensing techniques, it is possible to measure and collect the spectra of urban materials. Most spectral libraries consist of either spectra acquired indoors in a controlled lab environment or of spectra from afar using airborne systems accompanied with in situ measurements. Furthermore, most publicly available spectral libraries have, so far, not focused on facade materials but on roofing materials, roads, and pavements. In this study, we present an urban spectral library consisting of collected in situ material spectra with imaging spectroscopy techniques in the visible and near-infrared (VNIR) and short-wave infrared (SWIR) spectral range, with particular focus on facade materials and material variation. The spectral library consists of building materials, such as facade and roofing materials, in addition to surrounding ground material, but with a focus on facades. This novelty is beneficial to the community as there is a shift to oblique-viewed Unmanned Aerial Vehicle (UAV)-based remote sensing and thus, there is a need for new types of spectral libraries. The post-processing consists partly of an intra-set solar irradiance correction and recalculation of reference spectra caused by signal clipping. Furthermore, the clustering of the acquired spectra was performed and evaluated using spectral measures, including Spectral Angle and a modified Spectral Gradient Angle. To confirm and compare the material classes, we used samples from publicly available spectral libraries. The final material classification scheme is based on a hierarchy with subclasses, which enables a spectral library with a larger material variation and offers the possibility to perform a more refined material analysis. The analysis reveals that the color and the surface structure, texture or coating of a material plays a significantly larger role than what has been presented so far. The samples and their corresponding detailed metadata can be found in the Karlsruhe Library of Urban Materials (KLUM) archive

[1]  Julius O. Smith,et al.  Restoring a clipped signal , 1991, [Proceedings] ICASSP 91: 1991 International Conference on Acoustics, Speech, and Signal Processing.

[2]  Thomas H. Kolbe,et al.  Representing and Exchanging 3D City Models with CityGML , 2009 .

[3]  M. Santamouris,et al.  Using advanced cool materials in the urban built environment to mitigate heat islands and improve thermal comfort conditions , 2011 .

[4]  K. G. Fairbarn,et al.  Visible-Near Infrared (VNIR) and Shortwave Infrared (SWIR) Spectral Variability of Urban Materials , 2013 .

[5]  David Hernández-López,et al.  Image-based thermographic modeling for assessing energy efficiency of buildings façades , 2013 .

[6]  S. Hinz,et al.  CONCEPT FOR CLASSIFYING FACADE ELEMENTS BASED ON MATERIAL, GEOMETRY AND THERMAL RADIATION USING MULTIMODAL UAV REMOTE SENSING , 2017 .

[7]  Martin J. Wooster,et al.  Derivation of an urban materials spectral library through emittance and reflectance spectroscopy , 2014 .

[8]  B. Jutzi,et al.  Revisiting Existing Classification Approaches for Building Materials Based on Hyperspectral Data , 2017 .

[9]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[10]  Dimitri Bulatov,et al.  Physically-based Thermal Simulation of Large Scenes for Infrared Imaging , 2019, VISIGRAPP.

[11]  M. Santamouris,et al.  Using reflective pavements to mitigate urban heat island in warm climates - Results from a large scale urban mitigation project , 2017, Urban Climate.

[12]  David Belton,et al.  Classification and representation of commonly used roofing material using multisensorial aerial data , 2018 .

[13]  S. Myint,et al.  Exploring the effect of neighboring land cover pattern on land surface temperature of central building objects , 2016 .

[14]  Hermann Kaufmann,et al.  Determination of robust spectral features for identification of urban surface materials in hyperspectral remote sensing data , 2007 .

[15]  S. Hook,et al.  The ASTER spectral library version 2.0 , 2009 .

[16]  Sang Wook Lee,et al.  Spectral gradient: a material descriptor invariant to geometry and incident illumination , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[17]  Helmi Zulhaidi Mohd Shafri,et al.  DEVELOPMENT AND UTILIZATION OF URBAN SPECTRAL LIBRARY FOR REMOTE SENSING OF URBAN ENVIRONMENT , 2011 .

[18]  S. J. Sutley,et al.  USGS Digital Spectral Library splib06a , 2007 .

[19]  T. Oke The energetic basis of the urban heat island , 1982 .

[20]  Moritz Lauster,et al.  Development of the CityGML Application Domain Extension Energy for Urban Energy Simulation , 2015, Building Simulation Conference Proceedings.

[21]  Juan C. Jiménez-Muñoz,et al.  Emissivity mapping over urban areas using a classification-based approach: Application to the Dual-use European Security IR Experiment (DESIREX) , 2012, Int. J. Appl. Earth Obs. Geoinformation.

[22]  Soteris A. Kalogirou,et al.  Application of infrared thermography for the determination of the overall heat transfer coefficient (U-Value) in building envelopes , 2011 .

[23]  Thomas H. Kolbe,et al.  City-Wide Total Energy Demand Estimation of Buildings using Semantic 3D City Models and Statistical Data , 2013 .

[24]  Stefan A. Robila,et al.  An Investigation of Spectral Metrics in Hyperspectral Image Preprocessing for Classification , 2005 .

[25]  D. Roberts,et al.  Hierarchical Multiple Endmember Spectral Mixture Analysis (MESMA) of hyperspectral imagery for urban environments , 2009 .

[26]  Karl Pearson F.R.S. LIII. On lines and planes of closest fit to systems of points in space , 1901 .

[27]  Elia Quirós,et al.  Semiautomatic detection and classification of materials in historic buildings with low-cost photogrammetric equipment , 2017 .

[28]  A. Okujeni,et al.  Imaging Spectroscopy of Urban Environments , 2018, Surveys in Geophysics.

[29]  S. Gaffin,et al.  Positive effects of vegetation: urban heat island and green roofs. , 2011, Environmental pollution.

[30]  S. P. Lloyd,et al.  Least squares quantization in PCM , 1982, IEEE Trans. Inf. Theory.

[31]  Xavier Briottet,et al.  Spectral Band Selection for Urban Material Classification Using Hyperspectral Libraries , 2016 .

[32]  Geoffrey E. Hinton,et al.  Visualizing Data using t-SNE , 2008 .

[34]  J. C. Price,et al.  Examples of high resolution visible to near-infrared reflectance spectra and a standardized collection for remote sensing studies , 1995 .

[35]  Changming Sun,et al.  Semi-automated infrared simulation on real urban scenes based on multi-view images. , 2016, Optics express.

[36]  Felix Hueber,et al.  Hyperspectral Imaging Techniques For Spectral Detection And Classification , 2016 .

[37]  N. Keshava,et al.  Distance metrics and band selection in hyperspectral processing with applications to material identification and spectral libraries , 2004, IEEE Transactions on Geoscience and Remote Sensing.

[38]  Cardona Alzate,et al.  Predicción y selección de variables con bosques aleatorios en presencia de variables correlacionadas , 2020 .

[39]  Eyal Ben-Dor,et al.  A spectral based recognition of the urban environment using the visible and near-infrared spectral region (0.4-1.1 µm). A case study over Tel-Aviv, Israel , 2001 .

[40]  W. M. Benzel,et al.  USGS Spectral Library Version 7 , 2017 .

[41]  Chein-I. Chang Hyperspectral Imaging: Techniques for Spectral Detection and Classification , 2003 .

[42]  M. Herold,et al.  Spectral characteristics of asphalt road aging and deterioration: implications for remote-sensing applications. , 2005, Applied optics.

[43]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[44]  Margaret E. Gardner,et al.  Spectrometry for urban area remote sensing—Development and analysis of a spectral library from 350 to 2400 nm , 2004 .

[45]  Emilio Galán Huertos,et al.  DESIREX 2008: estudio de la isla de calor en la Ciudad de Madrid , 2009 .

[46]  Fred A. Kruse,et al.  The Spectral Image Processing System (SIPS) - Interactive visualization and analysis of imaging spectrometer data , 1993 .

[47]  Karl Segl,et al.  Analysis of spectral signatures of urban surfaces for their identification using hyperspectral HyMap data , 2001, IEEE/ISPRS Joint Workshop on Remote Sensing and Data Fusion over Urban Areas (Cat. No.01EX482).

[48]  Kathrin J. Ward,et al.  Heat waves and urban heat islands in Europe: A review of relevant drivers. , 2016, The Science of the total environment.