Determination and verification of robust spectral features for an automated classification of sealed urban surfaces.

Abstract

Urban surface materials have an immense influence on ecological conditions of urban areas. In this context, frequent material-oriented identification of urban surfaces can support municipal authorities in planning processes. This requires hyperspectral remote sensing data that are characterized by high spectral and spatial resolution. For full exploitation of the information content, detailed spectral knowledge of urban surfaces is required and automated methods are needed for effective processing of those data. In this study, a methodology for the determination and validation of materials-specific spectral features has been developed that can be used for automated identification of urban surface materials. It is demonstrated for representative urban surfaces in the cities of Dresden and Potsdam, Germany. First, an urban spectral library consisting of field and image spectra was developed that has been analyzed in regard to typical spectral features and their variations. Based on a visual analysis, robust features that are highly independent of spectral variations caused by e.g. illumination and degradation have been selected. The quality of these features has been validated in regard to an improved separability of urban surface materials analyzing a confusion matrix within a classification process. For this purpose, the features were described numerically using adapted feature functions. The results of the classification show a very good separability especially for predominantly bright urban surfaces, but also for darker materials with weak reflectance features. Even differentiation between spectrally similar urban surfaces was feasible. The derived robust spectral features reduce the need for test-site-specific training information. This way, the developed methodology represents an important step towards fully-automated identification of urban surfaces.