A New Algorithm for Void Filling in a DSM from Stereo Satellite Images in Urban Areas
| Autor: | Pablo d'Angelo, Peter Reinartz, Jiaojiao Tian, Z. Gharib Bafghi |
|---|---|
| Přispěvatelé: | Halounova, L., Šafář, V., Toth, C. K., Karas, J., Huadong, G., Haala, N., Habib, A., Reinartz, Peter, Tang, X., Li, J., Armenakis, C., Grenzdörffer, G., le Roux, P., Stylianidis, S., Blasi, R., Menard, M., Dufourmount, H., Li, Z. |
| Jazyk: | angličtina |
| Rok vydání: | 2016 |
| Předmět: |
lcsh:Applied optics. Photonics
Void (astronomy) 010504 meteorology & atmospheric sciences Computer science Mean-Shift Segmentation Multispectral image 0211 other engineering and technologies Stereo matching 02 engineering and technology 01 natural sciences lcsh:Technology Orthorectification DSM Void Filling Computer vision 021101 geological & geomatics engineering 0105 earth and related environmental sciences Photogrammetrie und Bildanalyse Stereo Satellite Images Filling-in business.industry lcsh:T Orthophoto lcsh:TA1501-1820 Image segmentation Thresholding lcsh:TA1-2040 Artificial intelligence business lcsh:Engineering (General). Civil engineering (General) Algorithm Change detection |
| Zdroj: | ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol III-1, Pp 55-61 (2016) |
| Popis: | Digital Surface Models (DSM) derived from stereo-pair satellite images are the main sources for many Geo-Informatics applications like 3D change detection, object classification and recognition. However since occlusion especially in urban scenes result in some deficiencies in the stereo matching phase, these DSMs contain some voids. In order to fill the voids a range of algorithms have been proposed, mainly including interpolation alone or along with auxiliary DSM. In this paper an algorithm for void filling in DSM from stereo satellite images has been developed. Unlike common previous approaches we didn’t use any external DSM to fill the voids. Our proposed algorithm uses only the original images and the unfilled DSM itself. First a neighborhood around every void in the unfilled DSM and its corresponding area in multispectral image is defined. Then it is analysed to extract both spectral and geometric texture and accordingly to assign labels to each cell in the voids. This step contains three phases comprising shadow detection, height thresholding and image segmentation. Thus every cell in void has a label and is filled by the median value of its co-labelled neighbors. The results for datasets from WorldView-2 and IKONOS are shown and discussed. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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