Fusion of sensor data, knowledge sources and algorithms for extraction and classification of topographic objects

baltsavias, emmanuel p.

Bibliographic data

Monograph

Persistent identifier:: 856473650

Author:: Baltsavias, Emmanuel P.

Title:: Fusion of sensor data, knowledge sources and algorithms for extraction and classification of topographic objects

Sub title:: Joint ISPRS/EARSeL Workshop ; 3 - 4 June 1999, Valladolid, Spain

Scope:: III, 209 Seiten

Year of publication:: 1999

Place of publication:: Coventry

Publisher of the original:: RICS Books

Identifier (digital):: 856473650

Illustration:: Illustrationen, Diagramme, Karten

Language:: English

Usage licence:: Attribution 4.0 International (CC BY 4.0)

Publisher of the digital copy:: Technische Informationsbibliothek Hannover

Place of publication of the digital copy:: Hannover

Year of publication of the original:: 2016

Document type:: Monograph

Collection:: Earth sciences

Chapter

Title:: TECHNICAL SESSION 3 OBJECT AND IMAGE CLASSIFICATION

Document type:: Monograph

Structure type:: Chapter

Chapter

Title:: INCLUSION OF MULTISPECTRAL DATA INTO OBJECT RECOGNITION. Bea Csathó , Toni Schenk, Dong-Cheon Lee and Sagi Filin

Document type:: Monograph

Structure type:: Chapter

International Archives of Photogrammetry and Remote Sensing, Vol. 32, Part 7-4-3 W6, Valladolid, Spain, 3-4 June, 1999 Fig. 5. This stereo pair shows a small image patch from the low altitude flight. Laser points from the same region are projected back to the two images with their exterior orientation parameters. Viewed under a stereoscope, a vivid 3-D scene appears with the laser points on the top of the surface that is obtained by fusing the stereopair. The colored dots indicate different elevations, with red the lowest and blue the highest points. Note that blue points are on top of buildings. employed in aerial stereopairs, we may obtain surface discontinuities directly. This is because edges in aerial images may have been caused by breaklines in object space. Not all edges correspond to breaklines, but there is hardly a breakline that is not manifest as an edge in the image. Fusing surface features is actually a two step process. First, the images covering the same scene are processed using multiple image matching (Krupnik, 1996). Next, the surface obtained during image matching is fused with the laser surface. Obviously, the aerial imagery must be registered to the same object space the laser points are represented in. In turn, this requires an aerial triangulation. To achieve the best fit between the visible and laser surface, the aerial triangulation should be performed by incorporating the laser data (Jaw, 1999). Figure 5 illustrates the registration of the visible and the laser surface. Here, a small image patch from two overlapping photographs is shown, together with laser points that have been projected back from object space to the images based on their exterior orientation. Viewed under a stereoscope, one gets a vivid 3-D impression of the surface. The figure also demonstrates the distribution of laser points, which is rather random with respect to surface features. For example, features smaller than the (irregular) sampling of laser point may not be captured. Moreover, Figure 6 clearly supports the claim that breaklines, such as roof outlines, should be determined from the aerial imagery. The refined surface, obtained in a two step fusion process, is now analyzed for humps in an attempt to separate the topographic surface. Then, the difference between the refined and the topographic surface would result in what we call hump-objects of a certain vertical dimension. The prime motivation is to partition the object space, such that the subsequent surface segmentation is only performed in areas identified as humps. The segmentation is a multi-stage grouping process aimed at determining a hierarchy of edges and surface patches. As an example, breaklines are segmented Fig. 6. Superimposed on the aerial image are the results from classifying the multispectral imagery and from segmenting the laser surface. Pink areas indicate dark, non-vegetated regions, and yellow are bright, non- vegetated regions. Green refers to woody vegetation. Finally, blue are shaded areas. The red contours are derived from laser data and they indicate humps. The combination green (from multispectral) and hump (from laser) triggers the hypothesis for a tree, for example. A hump with planar surface patches and a non-vegetated region is used for a building hypothesis. The fusion of surface information from aerial imagery and laser scanning systems ought to take into account the strengths and weaknesses of the two sensors. The major advantage of laser measurements is the high density and high quality. However, breaklines and formlines must be extracted from irregularly spaced samples. If feature-based matching is

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