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 2 PREREQUISITES FOR FUSION / INTEGRATION: IMAGE TO IMAGE / MAP REGISTRATION

Document type:: Monograph

Structure type:: Chapter

Chapter

Title:: GEOCODING AND COREGISTRATION OF MULTISENSOR AND MULTITEMPORAL REMOTE SENSING IMAGES. Hannes Raggam, Mathias Schardt and Heinz Gallaun

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 Int( 26 3.3. Matching-Based Coregistration (MBC) 3.3.1. Method Matching-based coregistration is a general technique to match geometrically images or images and maps, independently of their imaging geometry or projection in case of geocoded datasets. An automatic image matching procedure is applied (pixel-by-pixel or at coarser intervals) between an input image and a reference image in order to find corresponding points in the two images. Therefore, automated image matching is the key tool for this type of coregistration. The scheme of MBC is shown in Figure 7 for an input image in its original geometry and a reference image given in map/ground geometry, e.g. an ortho-image. A general workflow of the MBC procedure is shown in Figure 8. It comprises the following processing steps: 1. An automatic image matching procedure is applied to the image pair to be registered. The output of this procedure is a so-called disparity map, which shows the shift vectors between reference image and input image pixel co ordinates. In order to increase the performance of the image matching, the following pre-processing steps may be applied to the data: • radiometric equalisation in case that the images are radiometrically different, as is often the case for multisensor and multitemporal images; • coarse geometric registration through measurement of a few tie-points in case that the images are significantly different in geometry. Both steps can be done implicitly inside the matching procedure. 2. In a second step the disparity vectors resulting from image matching are used to resample the input image to the reference image geometry. 3.3.2. Application Areas of MBC Areas of application of the MBC technique include: 1. Registration of multitemporal and multisensor images to the geometry of a selected reference image. 2. Fine-registration of geocoded multitemporal and multisensor images in case that there are still obvious mismatches between the individual images after geocoding. In such cases, even small geolocation errors may disturb further applications. 3. MBC may also be used to support fully automatic geocoding without any operators interaction. For that, an image may first be coarsely geocoded using an initial (coarse) parametric model (or even a polynomial rectification). In a second step, this coarse product may be geometrically refined using coregistration to an already geocoded image. Automatic image matching may also be applied in order to check the correspondence of any registered or geocoded images. The disparity vectors, which are achieved for such data will provide a neutral quality measure for the correspondence of the data, which obviously should be acceptably small. Grey value 3.4. i Automat to the ne radiomei matching and used Researcf pixel m2 moved c the resp differenc or featur The ma product matching is defin coefficie found in method, various 1 image, ii the searc minimur correspo in Paa: backmat be applit 3.5. i Methods Digital I multisen Buchroit also usei been de\ During t image g Remote Researcl geometr images. ERDAS, • Gee acq • Inte par; • Hig • Ste: ster • Gei froi • SA pha • DE

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