Proceedings, XXth congress: Proceedings, XXth congress

Altan, M. Orhan

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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

Yo) nd ES SEN, oM ims International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XXXV, Part B-YF. Istanbul 2004 and is therefore very complex and time-consuming. Another disadvantage is that for every sensor type different model and different processing software has to be established. These reasons motivated the replacement of the physical sensor models by generalised models (Tao and Hu, 2001). In 1989 Kratky suggested as a generalised model the use of polynomial Rational Functions that.can support real time implementations and provide accurate solutions. The Rational Function model transforms the image pixel coordinates through the ratios of polynomial of ground coordinates. In this project Space Imaging Inc. provided the Rational Function coefficients (RFCs) used in the orthorectification process. 3.2 The orthorectification processing Firstly the following input data were used to perform the orthorectification: — The RFCs provided by Space Imaging Inc. — The DTM that has a grid size of 25-m and covers all the study area — The GCPs obtained by GPS measurements and additional GCPs obtained by orthophotos of scale 1: 5000 in order to cover evenly all the study area The orthorectification with these data resulted in a large RMS error of about 35 m. Aiming to minimise this error a methodology was developed involving the following tasks: — Synthesis from the two existing DTMs of a more accurate DTM - Refinement of the RFCs according to the ground coordinates of the GCPs — Orthorectification separately in three parts of the image (the west, the east part of the city and the hilly region) 3.2.1 Synthesis of DTM: In order to improve the accuracy of the DTM within all the study area the existing DTMs were mosaicked. Taking into account that the orthorectification of areas with intense anaglyph is usually problematic, it was considered necessary to use a highly accurate DTM in the hilly region. Thus in this specific part of the image it was preferable to use the DTM which covers this specific region and is characterized by a better accuracy. The process of mosaicking the DTMs consisted of three tasks. Initially, the hilly part was extracted from the DTM that covers all the study area. The extraction was made to the base of the mountain along points that were found to have about the same elevation in both DTMs. The second task involved changing the grid-size of the DTM of the hilly part (20 m) to fit the grid-size of the other DTM (25 m) through the process of resampling. It was also necessary to cut the DTM of the hilly part near the points mentioned above, so as to have a small overlapping with the other DTM. Finally, the DTMs were mosaicked and as for the overlapping area it was chosen to take the elevation values from the DTM of the hilly part, with the better accuracy. 3.2.2 Refinement of the RFCs: The vendor-provided RF coefficients can be improved to provide a more accurate solution. Based on the known coordinates of the GCPs an affine transformation can be applied using as initial values the vendor- provided RFCs (Di et al., 2003). 187 3.2.3 Orthorectification in three parts of the image: The orthorectification processing was applied three times for three parts of the image. Due to the fact that images with an intense anaglyph ~~ demand special attention during their orthorectification, it was preferable to process the hilly part separately. In order to have a good distribution of GCPs the image was also processed separately as far as the west and the east part were concerned. In this way three orthorectified image parts were produced which were finally joined together through mosaicking. The same methodology was carried out for both the panchromatic and the multispectral image and led to an improved accuracy of about 3.5 m. Image parts No. RMS Control Check Control Check West part 23 10 3.7m 3.1 m East part 51 15 3.9m 3.7m Hilly part 29 10 29m 29m Table 2. The number of points and RMS errors of the orthorectification of each part of the image 3.3 The results of the orthorectification There are many factors that can influence the orthorectification processing and reduce the accuracy of the orthoimages. In this case the reasons can be found in the insufficient accuracy of the initial DTM, of the vendor-provided RFCs or in the different sources of GCPs (GPS and orthophotos). Using the methodology mentioned helped facing these problems and minimized the RMS error to a sufficient extend. In Table 3 the results of the orthorectification are listed according to the data being used and the great improvement of the accuracy is shown. DTM RFCs RMS Initial DTM Before the 35m refinement After the Image Whole image Whole image | Synthetic DTM s 10m refinement Three parts of Synthetic DTM After the 35m refinement the image Table 3. RMS errors of the orthorectification according to the data used 4. FUSION 4.1 The fusion of images In 1999 Wald defined data fusion as a formal framework that expresses the means and tools for the alliance of data originating from different sources; it aims at obtaining information of greater quality; the exact definition of “greater quality” will depend upon the application. Image fusion aims at the generation of a single image from multiple image data for the extraction of information of higher quality (Pohl, 1999). Within this framework this project focused on a type of image fusion, the image sharpening. In image sharpening, higher spatial resolution panchromatic data is fused with lower spatial

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