Quality assessment for geo‐spatial objects derived from remotely sensed data

Abstract

Airborne laser scanners and multi‐spectral scanners provide information on height and spectra that offer exciting possibilities for extracting features in complicated urban areas. We apply an object‐based approach to building extraction from image data in an approach that differs from conventional per‐pixel approaches. Since image objects are extracted based on the thematic and geometric components of objects, quality assessments will have to be made object‐based with respect to these components. The known per‐pixel‐based methods for assessing quality have been examined in the new situation as well as their limitations. A new framework for carrying out quality assessments by measuring the similarity between the results of feature extraction and reference data is proposed in this paper. The proposed framework consists of both per‐object and per‐pixel measures of quality, thus providing measures pertaining to qualitative and quantitative measurements of object quality from thematic and geometric aspects. The proposed framework and measures of quality have been applied to an assessment of the results of object‐based building extraction using high‐resolution laser data and multi‐spectral data in two test cases. The results show that the per‐object‐based method of assessing quality gives additional information to conventional per‐pixel, attribute‐only assessment methods.