The influence of urban structures on impervious surface maps from airborne hyperspectral data


Airborne hyperspectral data fulfills the high spectral and spatial resolution requirements of urban remote sensing applications. Its high spectral information content enables delineating impervious areas including the separation of built-up and non built-up surfaces, thus being of high relevance for many urban environmental applications. However, two phenomena related to surface structure negatively impact the accuracy of maps from such airborne data sets: (1) displaced buildings that lead to confusion between the class built-up and adjacent non built-up areas as a function of building height and view-angle; (2) urban street trees obscuring impervious surface underneath. Both effects have so far not been investigated from airborne hyperspectral data and potential sources of inaccuracy are usually not differentiated in analysis utilizing such data. Thus, the positive influence of hyperspectral information might have been undervalued in many cases. We set up an analysis scheme that allows for separately quantifying sources of error when producing land cover maps from urban areas. Given reliable cadastral information on building extent and street network, a detailed analysis for a relatively large Hyperspectral Mapper data set acquired over Berlin, Germany, was performed. Results show that both building displacement and impervious surface obscured by tree crowns are of great impact: at large view-angles, building displacement adds up to 16% error compared to nadir regions; more than 30% of the street area is classified as vegetation. Moreover, both effects show irregularities that prohibit empirical correction: misclassification due to building displacement also depends on view-direction, i.e. illumination properties and shadow, while the influence of trees differs significantly along streets and inside residential areas. Results from this work underline the necessity to consider all image processing steps when evaluating the accuracy and reliability of remote sensing products and they depict directions for future methodological development.

Remote Sensing of Environment
Patrick Hostert
Patrick Hostert
Principal Investigator