Morphological operation based dense houses extraction from DSM

This paper presents a method of reshaping and extraction of markers and masks of the dense houses from the DSM based on mathematical morphology (MM). Houses in a digital surface model (DSM) are almost joined together in high-density housing areas, and most segmentation methods cannot completely sepa...

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Bibliographic Details
Main Authors: Y. Li, L. Zhu, K. Tachibana, H. Shimamura
Format: Article
Language:English
Published: Copernicus Publications 2014-08-01
Series:The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Online Access:http://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XL-3/183/2014/isprsarchives-XL-3-183-2014.pdf
Description
Summary:This paper presents a method of reshaping and extraction of markers and masks of the dense houses from the DSM based on mathematical morphology (MM). Houses in a digital surface model (DSM) are almost joined together in high-density housing areas, and most segmentation methods cannot completely separate them. We propose to label the markers of the buildings firstly and segment them into masks by watershed then. To avoid detecting more than one marker for a house or no marker at all due to its higher neighbour, the DSM is morphologically reshaped. It is carried out by a MM operation using the certain disk shape SE of the similar size to the houses. The sizes of the houses need to be estimated before reshaping. A granulometry generated by <i>opening-by-reconstruction</i> to the NDSM is proposed to detect the scales of the off-terrain objects. It is a histogram of the global volume of the top hats of the convex objects in the continuous scales. The obvious step change in the profile means that there are many objects of similar sizes occur at this scale. In reshaping procedure, the slices of the object are derived by morphological filtering at the detected continuous scales and reconstructed in pile as the dome. The markers are detected on the basis of the domes.
ISSN:1682-1750
2194-9034