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In VHR(very high resolution) aerial images, shadows indicating height information are valuable for validating or detecting changes\udon an existing 3D city model. In the paper, we propose a novel and full automatic approach for shadow detection from VHR images.\udInstead of automatic thresholding, the supervised machine learning approach is expected with better performance on shadow detection,\udbut it requires to obtain training samples manually. The shadow image reconstructed from an existing 3D city model can provide free\udtraining samples with large variety. However, as the 3D model is often not accuracy, incomplete and outdated, a small portion of training\udsamples are mislabeled. The erosion morphology is provided to remove boundary pixels which have high mislabeling possibility from\udthe reconstructed image. Moreover, the quadratic discriminant analysis (QDA) which is resistant to the mislabeling is chosen. Further,\udtwo feature domains, RGB and ratio of the hue over the intensity, are analyzed to have complementary effects on better detecting\uddifferent objects. Finally, a decision fusion approach is proposed to combine the results wisely from preliminary classifications from\udtwo feature domains. The fuzzy membership is a confidence measurement and determines the way of making decision, in the meanwhile\udthe memberships are weighted by an entropy measurements to indicate their certainties. The experimental results on two cities in the\udNetherlands demonstrate that the proposed approach outperforms the two separate classifiers and two stacked-vector fusion approaches

the international archives of the photogrammetry, remote sensing and spatial information sciences/international archives of the photogrammetry, remote sensing and spatial information sciences

SHADOWDETECTION FROM VHR AERIAL IMAGES IN URBAN AREA BY USING 3D CITY MODELS AND A DECISION FUSION APPROACH