Segment-based characterization of roof surfaces using hyperspectral and laser scanning data

Abstract

Using remote sensing for urban applications makes high demands on the resolution of the used data - not only concerning its geometric resolution, in terms of ground sampling distance, but also concerning the spectral resolution, in terms of the number of narrow bands, allowing an almost continuous representation of the spectrum. In order to deal with the vari- ability and number of different surface materials with sometimes quite similiar spectral properties, hyperspectral data with its high spectral resolution seems to be mandatory for applications depending on classification of urban surface materials. A recent project of the Chair of Water Chemistry, Engler-Bunte-Institute (EBI), and the Institute of Photogrammetry and Remote Sensing (IPF) - both University of Karlsruhe - aims at the quantitative assessment of pollutants on urban surfaces by chemical analysis and image processing methods. Our research focus at IPF is the characterization of roof surfaces by combined use of hyperspectral and laser scanning data using a segment-based approach. The laser scanning data is primarily used for geometric characterization of the roof patches, but also in combination with the hyperspectral data for material classification. The hyperspectral data already gives rich information about the material, nevertheless the geometry of the roof surface restricts the possible material classes and therefore eases discrimination of materials with almost similar spectra. I. INTRODUCTION The assessment of pollutants on urban surfaces and their impact on the pollution load in rain runoffs is a small, but nevertheless important topic in the assessment of the influence of human activity on the status of surface waters and groundwater. Thus, the aim of our research project is not only to derive information on the amount of sealed surfaces in an urban area, but to derive a detailed surface material map. The necessary classes for our application are identified based on chemical measurements on reference roof surfaces, observing that different roof constructions/materials may have similar polluting behaviour. This allows merging of classes with respect to the resulting pollution, although they may have different spectral properties. One example are those material combinations including a bitumen layer and a covering layer from stone materials. The pure material-spectra-oriented clas- sification (cf. (1)) is in our approach supported by geometric clues of surface patches, thus combining geometric data from laser scanning and hyperspectral data for the characterization of roof segments. In the following, we give a short overview on related work. Section III introduces the input data. Our approach for the characterization of roof surfaces in urban areas is presented in Section IV. Recent results as well as a quantitative evaluation follow in Section V, finalized by the conclusions. II. RELATED WORK Laser scanning and hyperspectral data are often used exclu- sively, either to derive the geometry based on laser scanning data (cf. (2)) or to derive material maps based on hyperspectral data (cf. (1)). (3) use hyperspectral data (AVIRIS) in order to improve reconstruction results based on IFSAR, namely to mask vegetation areas, but the used data has only limited geo- metric resolution. In (4), they present results of hyperspectral data analysis for urban areas based on ROSIS and DAIS data, also discussing the impact of spectral and geometric resolution. (5) integrate Digital Surface Model (DSM) information in or- der to improve the results of hyperspectral classification based on HYDICE data. In their research the DSM, derived from aerial imagery, is applied for the discrimination of roofs and ground surfaces. The materials may have a similar spectrum, but they can be discriminated based on the height information. (6) show material mapping techniques based on deterministic similarity measures for spectral matching to separate target from non-target pixels in urban areas.