A Learning-Based Resegmentation Method for Extraction of Buildings in Satellite Images

Date

2014-12-01

Author

Dikmen, Mehmet
Halıcı, Uğur

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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This letter introduces a new method for building extraction in satellite images. The algorithm first identifies the shadow segments on an oversegmented image, and then neighboring shadow segments, which are assumed to be cast by a single building, are merged. Next, candidate regions where buildings most likely occur are detected by using these shadow regions. Along with this information, closeness to shadows in illumination direction and spectral properties of segments are used to classify them as belonging to a "building" or not. Then, a resegmentation is performed by merging only the neighboring segments, which are classified as building. Finally, postprocessing is performed to eliminate some false building segments. The approach was tested on several Google Earth images, and the results are found to be promising.

Subject Keywords

Building extraction, Feature extraction, Image classification, Image segmentation, Remote sensing, Satellite images

URI

https://hdl.handle.net/11511/33386

Journal

IEEE GEOSCIENCE AND REMOTE SENSING LETTERS

DOI

https://doi.org/10.1109/lgrs.2014.2321658

Collections

Department of Electrical and Electronics Engineering, Article

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Citation Formats

M. Dikmen and U. Halıcı, “A Learning-Based Resegmentation Method for Extraction of Buildings in Satellite Images,” IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, pp. 2150–2153, 2014, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/33386.