Segmentation of shadows and water bodies in high resolution images using ancillary data
2016 (English)In: Proc. 16th International Multidisciplinary Scientific GeoConference, 2016, Vol. 1, 827-834 p.Conference paper (Refereed)
High spatial resolution imagery is often affected by shadows, both in urban environments with large variations in surface elevation and in vegetated areas. It is a common bias in classification when waters and shadows are registered as the same area. The radiometric response for the shadowed regions should be restored prior to classification. To enable that, separate classes of non-shadowed regions and shadowed areas should be created. Previous work on water extraction using low/medium resolution images, mainly faced two difficulties. Firstly, it is difficult to obtain accurate position of water boundary and secondly, shadows of elevated objects e.g. buildings, bridges, towers and trees are a typical source of noise when facing water extraction in urban regions. In high resolution images the problem of separation water and shadows becomes more prominent since the small local variation of intensity values gives rise to misclassification. This paper proposes a robust method for separation of shadowed areas and water bodies in high spatial resolution imagery using hierarchical method on different scales combined with classification of PCA (Principal Component Analysis) bands, which reduces the effects of radiometric and spatial differences that is commonly associated with the pixel-based methods for multisource data fusion. The method uses ancillary data to aid in classification of shadows and waters. The proposed method includes three steps: segmentation, classification and postprocessing. To achieve robust segmentation, we apply the merging region with three features (PCA bands, NSVDI (Normalized Saturation-value Difference Index) and height data). NSVDI discriminates shadows and some water. In the second step we use hierarchic region based classification to identify water regions. After that step candidates for water pixels are verified by the LiDAR DEM data. As a last step we consider shape parameters such as compactness and symmetry to completely remove shadows.
Place, publisher, year, edition, pages
2016. Vol. 1, 827-834 p.
shadows, water bodies, objects, segmentation, high-resolution
Computer and Information Science
Research subject Computerized Image Processing
IdentifiersURN: urn:nbn:se:uu:diva-309555DOI: 10.5593/SGEM2016/B21/S08.104ISBN: 978-619-7105-58-2OAI: oai:DiVA.org:uu-309555DiVA: diva2:1052092
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