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:: INVESTIGATION OF SYNERGY EFFECTS BETWEEN SATELLITE IMAGERY AND DIGITAL TOPOGRAPHIC DATABASES BY USING INTEGRATED KNOWLEDGE PROCESSING. Dietmar Kunz

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 147 Fig. 9. Result of the knowledge based segmentation for the object class ‘settlement’. Assuming that the appearance of landuse classes in satellite images can be characterized by a significant accumulation of pixels with typical reflectance values compared to the surrounding areas, the spatial distance of pixels with similar reflectance values can be a criterion to find object boundaries. As an example, Fig. 3 shows an original detail of a Landsat TM image and Fig. 4 the significant vegetation-free pixels (in black) as candidates for the object class 'settlement'. A successful method to determine the object contours is the use of triangulation networks, e.g. Delaunay triangulation, of the marked pixels (see Fig. 5). Based on all DLM200 settlement objects in the satellite image, statistics of the triangle perimeters (mean values, standard deviations) can be determined. This is used as a structural parameter related to the 'compactness' of the object. On the other hand, valid triangles can be extracted by means of this perimeter statistic (Fig. 6). After a fusion of all adjacent valid triangles, the object contours can be obtained (Fig. 7). The same procedure can be applied to all other - also the homogeneous ones - object classes. Fig. 8 shows the enclosing object contours for the accumulation of the marked pixels having a specific vegetation index and the corresponding DLM objects in this area. The result of the segmentation process is a symbolic scene description with polygonal objects of the three land use classes. In the last step of feature extraction, texture parameters inside the object contours are calculated. A very simple but efficient parameter is the standard deviation of the grey values inside the objects. Because of some disturbances, for instance caused by errors in the topographic database or errors in the geocoding of the satellite image, a histogram analysis of the grey value distribution is needed. In this histogram analysis, the peak of the grey value distribution is extracted and the minor peaks are eliminated. The standard deviation is estimated for this changed distribution. The result is a Modified Standard Deviation, which can be used as robust texture parameter (Fig. 10). Also test series with the well-known texture parameters proposed by Haralick and Shapiro (1992) have been carried out. The tests have shown that homogeneity is one of the best parameters to distinguish between the above defined object classes (Fig. 11). With both texture parameters the separability of homogeneous from inhomogeneous classes is very good. This is important for the spectrally similar classes ‘settlement’ and ‘agriculture’. n m homogeneity = xi j=l i=l w(i,j) 1 + (g(i) — g(j)) 2 w(i, j) ...element of the co - occurrence matrix g(i), g(j) ...grey values in a row / column Neigbourhood relations for an image object are determined by storing the identification code numbers of adjacent objects.

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