PROJECT TITLE :
Normalization of TanDEM-X DSM Data in Urban Environments With Morphological Filters
The TanDEM-X mission (TDM) may be a spaceborne radar interferometer that delivers a global digital surface model (DSM) with an unprecedented spatial resolution. This enables resolving objects higher than ground such as buildings. Extracting and characterizing those objects in an automatic manner represents a difficult drawback but opens simultaneously a broad vary of huge-space applications. In this paper, we tend to discuss and evaluate the suitability of morphological filters (MFs) for the derivation of normalized DSMs from the TDM in complex urban environments and introduce a unique region-growing-primarily based progressive MF procedure. This approach is jointly proposed and can be combined with a postclassification processing scheme to specifically allow for a viable reconstruction of urban morphology in a very challenging terrain. The filter approach contains a multistep procedure using ideas of morphological image filtering, region growing, and interpolation techniques. Thus, it extends the idea of progressive MFs. The latter aim to spot nonground pixels in the DSM by gradually increasing the size of a structuring component and applying iteratively an elevation distinction threshold. When the identification of initial nonground pixels, here, potential nonground pixels are identified at intervals each iteration, and their similarity with respect to neighboring nonground pixels is assessed. Pixels are finally labeled as nonground if a constraint is fulfilled. The postclassification processing scheme adapts techniques of object-based image analyses to any refine regions of classified nonground pixels. Digital terrain models are subsequently generated by interpolating between identified ground pixels. Experimental results are obtained for settlement areas that cover large parts of the cities of Izmir (Turkey) and Wuppertal (Germany). They confirm the aptitude of the proposed approaches for a discount of omission errors compared to basic MF-based mostly methods when classifying ground pixe- s, which is favorable in a mountainous terrain with steep slopes.
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