Shadow detection from VHR aerial images in urban area by using 3D city models and a decision fusion approach

Shadow detection from VHR aerial images in urban area by using 3D city models and a decision fusion approach

Zhou, K. (TU Delft Optical and Laser Remote Sensing)
Gorte, B.G.H. (TU Delft Optical and Laser Remote Sensing)

Li, D. (editor)
Gong, J. (editor)
Yang, B. (editor)

2017-09-12

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 However, as the 3D model is often not accuracy, incomplete and outdated, a small portion of training samples are mislabeled.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.

3D city model
Decision fusion
Entropy
Free training samples
Fuzzy membership
Mislabel
QDA
VHR aerial images

http://resolver.tudelft.nl/uuid:37df6789-f7f3-4d71-82d4-6ffefc77905d

https://doi.org/10.5194/isprs-archives-XLII-2-W7-579-2017

ISPRS Geospatial Week 2017, 42

The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences,, XLII-2/W7

Institutional Repository

conference paper

© 2017 K. Zhou, B.G.H. Gorte