Proceedings, XXth congress: Proceedings, XXth congress

Altan, M. Orhan

Bibliographic data

Multivolume work

Persistent identifier:: 1663674213

Title:: Proceedings, XXth congress

Sub title:: Istanbul, 12 - 23 July 2004

Year of publication:: 2004

Place of publication:: Istanbul

Publisher of the original:: [Verlag nicht ermittelbar]

Identifier (digital):: 1663674213

Language:: English

Additional Notes:: Erscheinungsdatum des Originals ist aus dem Copyrightjahr ermittelt.; Auch bezeichnet als XXth International Congress for Photogrammetry and Remote Sensing

Editor:: Altan, M. Orhan

Corporations:: International Society for Photogrammetry and Remote Sensing, Congress, 20., 2004, Istanbul; International Society for Photogrammetry and Remote Sensing, Commission Primary Data Acquisition

Adapter:: International Society for Photogrammetry and Remote Sensing, Congress, 20., 2004, Istanbul; International Society for Photogrammetry and Remote Sensing, Commission Primary Data Acquisition

Founder of work:: International Society for Photogrammetry and Remote Sensing, Congress, 20., 2004, Istanbul; International Society for Photogrammetry and Remote Sensing, Commission Primary Data Acquisition

Other corporate:: International Society for Photogrammetry and Remote Sensing, Congress, 20., 2004, Istanbul; International Society for Photogrammetry and Remote Sensing, Commission Primary Data Acquisition

Document type:: Multivolume work

Volume

Persistent identifier:: 166368779X

Title:: Proceedings, XXth congress

Scope:: IV, 226 Seiten

Year of publication:: 2004

Place of publication:: Istanbul

Publisher of the original:: [Verlag nicht ermittelbar]

Identifier (digital):: 166368779X

Illustration:: Illustrationen, Diagramme

Signature of the source:: ZS 312(35,B8)

Language:: English

Additional Notes:: Erscheinungsdatum des Originals ist aus dem Copyrightjahr ermittelt.

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

Editor:: Altan, M. Orhan

Corporations:: International Society for Photogrammetry and Remote Sensing, Congress, 20., 2004, Istanbul; International Society for Photogrammetry and Remote Sensing

Adapter:: International Society for Photogrammetry and Remote Sensing, Congress, 20., 2004, Istanbul; International Society for Photogrammetry and Remote Sensing

Founder of work:: International Society for Photogrammetry and Remote Sensing, Congress, 20., 2004, Istanbul; International Society for Photogrammetry and Remote Sensing

Other corporate:: International Society for Photogrammetry and Remote Sensing, Congress, 20., 2004, Istanbul; International Society for Photogrammetry and Remote Sensing

Publisher of the digital copy:: Technische Informationsbibliothek Hannover

Place of publication of the digital copy:: Hannover

Year of publication of the original:: 2019

Document type:: Volume

Collection:: Earth sciences

Chapter

Title:: URBAN ORTHOIMAGE ANALYSIS GENERATED FROM IKONOS DATA S. Siachalou

Document type:: Multivolume work

Structure type:: Chapter

C le le S, at International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XXXV, Part B-YF. Istanbul 2004 the comparison of the two ‘high-pass’ images a histogram matching of the pan-sharpened image to the panchromatic orthoimage was indispensable. In Table 6 the correlation coefficients between the panchromatic band the pan-sharpened bands are presented. Given that the ideal value is 1, it can be noted that the pan- sharpened image preserves the characteristics of the initial high- resolution image it came from. Correlation coefficient between ‘high-pass’ panchromatic — ‘high-pass’ pan-sharpened image Pan-sharpened BANDS / 2 3 4 Panchromatic BAND 0.99 0.99 0.99 0.99 Table 6. Criteria on the spatial evaluation of the pan-sharpened image 5. CLASSIFICATION Having a pan-sharpened image of good spectral and spatial quality enables us to derive reliable thematic information through the process of classification. Classification of urban environment plays a key role in Urban Area Land-use Mapping, Urban Planning and Management, Establishment and Revision of GIS Database, Environment and Disaster Monitoring and Establishment of Telecommunication Network Station (Yu et al., 2002). With the development of high-resolution satellites it is now possible to collect and map thematic information from images of urban areas on large scales. However, high-resolution data does not mean high classification accuracy. In such cases, classification is more difficult due to the heterogeneity of urban Structures. 5.1 The procedure of classification At first, a supervised classification with the method of maximum-likelihood classification was applied to the pan- sharpened image. Although a careful and detailed training was carried out, problems arose during the evaluation of the samples. Specifically, in the resulting classified image some streets were mixed up with buildings and territories of bare soil were misclassified as burnt forest. For this reason a different approach was decided. In urban areas roofs, streets and pavements are built of a similar material, so their similar reflectance creates problems during the classification process. The pan-sharpened image was separated to the urban part and the hilly tree-covered part and the classification was applied to these two image parts. Firstly, the set of classes was selected for each part and consists of streets, roofs, tiles, bare soil, low vegetation, trees and shadow for the urban part and trees, burnt forest, streets and rural streets for the hilly part. After the selection of the samples for each class, they were assessed through their histograms and the diagrams of their means and found to be representative and satisfactory. A fuzzy classification was introduced for both image parts and a distance file was created. The fuzzy approach was preferred because it allows more information on the partial class membership to be made available. The classified images were 189 chosen to have two layers of which Layer 1 contains class values for the best classification, Layer 2 for the second best. The pixels of the distance file consist of the values of the distance of the class means. With the help of the classified images and the distance files the Fuzzy Convolution operator creates a single classification layer by calculating the total weighted inverse distance of all the classes in a window of pixels, in this case a 3 X 3. Then it assigns the center pixel in the class with the largest total weighted inverse distance over both fuzzy classification layers. This reduces the phenomenon of 'salt and pepper' in the classified image which the mixed pixels produce (Erdas, 1999). The final classified image is presented in Figure 7. Classes Low vegetation Streets Bare soil Trees Burnt forest Roofs ’ Tiles Shadows Rural streets MILITE D Figure 7. The classified image resulted from the application of the supervised classification 5.2 Accuracy assessment of the classification The procedure of classification is completed when its accuracy is estimated. The error of the classification, which expresses the misclassification of some pixels, determines the degree of success of the procedure. For this reason a comparison is made between classified data and ground reference data and the error matrix, the accuracy report and the Kappa coefficient are calculated. The results are presented in Table 4. ACCURACY RESULTS FOR THE URBAN IMAGE PART ERROR MATRIX Classes |Low veg.|Streets|Bare soil| Trees | Shadows| Roofs | Tiles Low veg. 0 | | 0 I 0 0 Streets 7 8 0 0 ] | 0 Bare soil 0 0 8 0 0 | l Trees 0 0 0 10 0 0 0 Shadows 0 0 0 0 10 0 0 Roofs 0 2 0 0 2 6 0 Tiles 0 0 | 0 1 0 8 Toll test ÿ nl. 10 15 8 9 pixels Overall Accuracy = 84.43% Kappa coefficient = 78.33% ACCURACY RESULTS FOR THE HILLY IMAGE PART ERROR MATRIX Classes Trees Burnt forest | Streets Rural streets Trees 2 0 0 Burnt forest 0 8 0 0 Streets 1 0 5 2 Rural streets 0 | 0 Total test pixels 7 11 5 9 Overall Accuracy = 81.25% Kappa coefficient = 75.00%

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