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 4 FUSION OF SENSOR-DERIVED PRODUCTS

Document type:: Monograph

Structure type:: Chapter

Chapter

Title:: AUTOMATIC CLASSIFICATION OF URBAN ENVIRONMENTS FOR DATABASE REVISION USING LIDAR AND COLOR AERIAL IMAGERY. N. Haala, V. Walter

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 urban environment the classification results can be improved considerably, if the normalized DSM representing the local height above terrain is introduced as an additional database during classification. □ Buildings Roads Trees Grass-covered Fig. 8. Maximum likelihood classification based on CIR imagery and laser data. 3.4. Further Improvement by Shadow Analysis One problem, when classifying areas with rough surfaces and steep slopes as they can occur in urban areas, is the change of spectral reflectance due to the presence of shadow areas. In order to avoid these problems, the shadow areas can be derived automatically based on the given DSM and used as an additional source of information during the classification process. The result of the automatic generation of shadow areas is given in Figure 9. Fig. 9. Shadow areas as derived from DSM. For the automatic detection of shadow areas, the local height, which is provided by the laser DSM as well as the elevation and azimuth of the sun at the time of image acquisition is required. The elevation and azimuth of the sun can either be determined manually, by an interactive measurement of an edge of a shadowed area in the image and the corresponding object height in the normalized DSM, or derived automatically from the geographical latitude and longitude of the captured area and the time of image acquisition. The improvement of the classification when using predefined shadow areas is demonstrated in Figures 11 and 12, using the test area depicted in Figure 10. In Figure 11, the misclassifications in shadow areas are clearly visible. These misclassifications can be avoided, if the class shadow is determined in advance based on the analysis of the given DSM and excluded from further classification like it is demonstrated in Figure 12. Fig. 10. Ortho-image. Buildings Grass-covered Roads Trees Not classified Fig. 11. Classification without using shadow areas. In order to avoid the shadow class in the final result, the approach can be further refined by splitting each of the landuse classes into one separate class for shadow areas and one landuse class for non-shadow areas. This leads to a significant improvement of the results because the pixels in shadow areas have completely different spectral characteristics than the ones

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