Building detection with multi-view colour infrared imagery.

This paper presents an automatic building detection approach exploiting colour infrared (CIR) imagery. A Digital Surface Model (DSM) is first extracted by photogrammetry with a proprietary development including multiple views. Then a Digital Terrain Model (DTM) is derived from the DSM by selecting the lowest regions with slowly varying elevation. A normalised DSM, difference of the DSM and derived DTM highlights building candidates thanks to a simple threshold. In order to reduce false positives due to trees, a vegetation mask is obtained from the NDVI of the ortho-rectified CIR image. A specific procedure was designed to handle the problematic shadow areas where the NDVI criterion often misses vegetation. The proposed building detection approach has been applied to the Vaihingen data set of the ISPRS benchmark and validated by the results of its evaluation procedure. We plan to integrate the new DTM extraction and shadow adapted vegetation mask, main contributions of this paper, to our project about change detection for database revision for the Belgian National Geographic Institute.