A comparative study on shadow compensation of color aerial images in invariant color models

In urban color aerial images, shadows cast by cultural features may cause false color tone, loss of feature information, shape distortion of objects, and failure of conjugate image matching within the shadow area. This paper presents an automatic property-based approach for the detection and compensation of shadow regions with shape information preserved in complex urban color aerial images for solving problems caused by cast shadows in digital image mapping. The technique is applied in several invariant color spaces that decouple luminance and chromaticity, including HSI, HSV, HCV, YIQ, and YC_bC_r models. Experimental results from de-shadowing color aerial images of a complex building and a highway segment in these color models are evaluated in terms of visual comparisons and shadow detection accuracy assessments. The results show the effectiveness of the proposed approach in revealing details under shadows and the suitability of these color models in de-shadowing urban color aerial images.