No abstract

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.

ABSTRACT:This paper presents a concept for classification of facade elements, based on the material and the geometry of the elements in addition to the thermal radiation of the facade with the usage of a multimodal Unmanned Aerial Vehicle (UAV) system. Once the concept is finalized and functional, the workflow can be used for energy demand estimations for buildings by exploiting existing methods for estimation of heat transfer coefficient and the transmitted heat loss. The multimodal system consists of a thermal, a hyperspectral and an optical sensor, which can be operational with a UAV. While dealing with sensors that operate in different spectra and have different technical specifications, such as the radiometric and the geometric resolution, the challenges that are faced are presented. Addressed are the different approaches of data fusion, such as image registration, generation of 3D models by performing image matching and the means for classification based on either the geometry of the object or the pixel values. As a first step towards realizing the concept, the result from a geometric calibration with a designed multimodal calibration pattern is presented.

ABSTRACT:Classification of materials found in urban areas using remote sensing, in particular with hyperspectral data, has in recent times increased in importance. This study is conducting classification of materials found on building using hyperspectral data, by using an existing spectral library and collected data acquired with a spectrometer. Two commonly used classification algorithms, Support Vector Machine and Random Forest, were used to classify the materials. In addition, dimensionality reduction and band selection were performed to determine if selected parts of the full spectral domain, such as the Short Wave Infra-Red domain, are sufficient to classify the different materials. We achieved the best classification results for the two datasets using dimensionality reduction based on a Principal Component Analysis in combination with a Random Forest classification. Classification using the full domain achieved the best results, followed by the Short Wave Infra-Red domain.

<p><strong>Abstract.</strong> As more cities are starting to experience the urban heat islands effect, knowledge about the energy emitted from building roofs is of primary importance. Since this energy depends both on roof orientations and materials, we tackled both issues by analysing sensor data from multispectral, thermal infrared, high-resolution RGB, and airborne laser datasets (each with different spatial resolutions) of a council in Perth, Australia. To localise the roofs, we acquired building outlines that had to be updated using the normalised digital surface model, the NDVI and the planarity. Then, we computed a semantic 3D model of the study area, with roof detail analysis being a particular focus. The main objective of this study, however, was to classify three commonly used roofing materials: <i>Cement tiles</i>, <i>Colorbond</i> and <i>Zincalume</i> by combining the multispectral and thermal infrared image bands while the high-resolution RGB dataset was used to provide additional information about the roof texture. Three types of image segmentation approaches were evaluated to assess any differences while performing the material classification; pixel-wise, superpixel-wise and building-wise image segmentation. Due to the limited amount of labelled data, we extended the dataset by labelling data ourselves and merged <i>Colorbond</i> and <i>Zincalume</i> into one separate class. The supervised classifier Random Forest was applied to all reasonable configurations of segmentation kinds, numbers of classes, and finally, keeping track of the added value of principal component analysis.</p>