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 6 INTEGRATION OF IMAGE ANALYSIS AND GIS

Document type:: Monograph

Structure type:: Chapter

Chapter

Title:: AUTOMATIC RECONSTRUCTION OF ROOFS FROM MAPS AND ELEVATION DATA. U. Stilla, K. Jurkiewicz

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 141 The result of the first step is used to mask the elevation data (Fig. 3). In this way, we obtain different elevation data for buildings and non-buildings. Calculating the mean height for each building object of the map, a coarse 3D-description is constructed by prismatic models. These wire-frame models are transformed into a surface model using an automatic tri angulation. 4.2. Flat Roofs In case (i), the position of the peak’s maximum is found and this value is assigned as the height value to the prismatic object. In case (ii), the minor peaks with a certain height distance to the main peak are searched for (Fig. 6c). Then, a minimum in the histogram between two consecutive peaks (main and minor ones) is found and a threshold value is calculated. These thresholds are used to segment the elevation data (Fig. 6d-f). The segments are labelled and examined for compactness (circumference/area). Segments, which are too small or not compact, are not taken into consideration for further analysis (Fig. 6e). A compact segment of a size greater than a minimum area confirms the hypothesis of a superstructure (Fig. 6f) and the contour is accepted (Fig. 6g). Fig. 4. City model of prismatic objects (buildings). 4. ROOF RECONSTRUCTION Depending on the application, buildings should be reconstructed with a varying level of detail. In this research, a description of the buildings as shown in Fig. 4 is aimed for. The roofs should be reconstructed from elevation data. Simple roof structures show characteristic histograms (Fig. 5). Fig. 5. Characteristic height histograms of simple roofs. Flat roofed buildings show a significant peak in the histogram, belonging to an area corresponding to the building outline. If a flat roofed building has a flat superstructure (e.g. penthouse) the histogram shows an additional peak above the main peak. Simple gabled roofs show a rectangular histogram. The width of the rectangle depends on the slope of the roof. A hip roof shows a trapezoidal histogram. The length of the ridge determines the height (frequency) of the right side of the histogram. A cropped hip roof shows a mixture of rectangle and trapezoidal form. Since the ideal histogram forms are not present in real data, the discrimination of sloped roofs by their histograms will be hardly possible. 4.1. Roof Hypotheses Based on the histogram of heights, flat roofs and sloped roofs are discriminated. If the distance between minimum and maximum height is smaller than a threshold, a flat roof without superstructures is hypothesized (case (i)). If the distance is large enough, the distribution is examined using the entropy relative to the elevation range. If this value is low, a flat roof with a flat superstructure is hypothesized (case (ii)), otherwise, sloped roof parts are assumed (case (iii)). Fig. 6. Reconstruction of a flat roof with a superstructure. In the following vectorization step of the contour chain, we first try to fit the contour by a rectangle (Fig. 6). If the assessment of the fit is lower than a given threshold, the contour is rotated to a coordinate system parallel to the major orientation of the building. After projecting the contour points to the coordinate axes, peaks are searched in the histogram to describe the contour by a right-angled polygon (e.g. L-structure). If this approximation is also insufficient, the contour is approximated

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