Print Email Facebook Twitter Shadow detection from VHR aerial images in urban area by using 3D city models and a decision fusion approach Title Shadow detection from VHR aerial images in urban area by using 3D city models and a decision fusion approach Author Zhou, K. (TU Delft Optical and Laser Remote Sensing) Gorte, B.G.H. (TU Delft Optical and Laser Remote Sensing) Contributor Li, D. (editor) Gong, J. (editor) Yang, B. (editor) Date 2017-09-12 Abstract In VHR(very high resolution) aerial images, shadows indicating height information are valuable for validating or detecting changes on an existing 3D city model. In the paper, we propose a novel and full automatic approach for shadow detection from VHR images. Instead of automatic thresholding, the supervised machine learning approach is expected with better performance on shadow detection, but it requires to obtain training samples manually. The shadow image reconstructed from an existing 3D city model can provide free training samples with large variety. However, as the 3D model is often not accuracy, incomplete and outdated, a small portion of training samples are mislabeled. The erosion morphology is provided to remove boundary pixels which have high mislabeling possibility from the reconstructed image. Moreover, the quadratic discriminant analysis (QDA) which is resistant to the mislabeling is chosen. Further, two feature domains, RGB and ratio of the hue over the intensity, are analyzed to have complementary effects on better detecting different objects. Finally, a decision fusion approach is proposed to combine the results wisely from preliminary classifications from two feature domains. The fuzzy membership is a confidence measurement and determines the way of making decision, in the meanwhile the memberships are weighted by an entropy measurements to indicate their certainties. The experimental results on two cities in the Netherlands demonstrate that the proposed approach outperforms the two separate classifiers and two stacked-vector fusion approaches. Subject 3D city modelDecision fusionEntropyFree training samplesFuzzy membershipMislabelQDAVHR aerial images To reference this document use: http://resolver.tudelft.nl/uuid:37df6789-f7f3-4d71-82d4-6ffefc77905d DOI https://doi.org/10.5194/isprs-archives-XLII-2-W7-579-2017 Source ISPRS Geospatial Week 2017, 42 Series The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences,, XLII-2/W7 Part of collection Institutional Repository Document type conference paper Rights © 2017 K. Zhou, B.G.H. Gorte Files PDF isprs_archives_XLII_2_W7_ ... 9_2017.pdf 3.58 MB Close viewer /islandora/object/uuid:37df6789-f7f3-4d71-82d4-6ffefc77905d/datastream/OBJ/view