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 GCPs and the method of measuring their pixel coordinates. The localisation accuracy usually lies in the 0.5-1 pixel range. 3.2.2. Geocoding Through geocoding, image data are transformed to a common reference map projection, e.g. defined through topographic maps being used. This is the standard procedure for remote sensing images. Displacement errors caused by topographic relief are removed through the integration of a DEM (in such a case, also the terms orthorectification and differential orthorectification are used). 2. As the above transformation yields an irregular raster of points, a proper grey value interpolation/resampling has to be applied in order to get a regular raster. Grey value Figure 5 shows a general scheme of the image geocoding procedure. Based on a DEM and an optimised imaging model (see above), geocoding can be performed in two basic processing steps: 1. First, for each output pixel which defines a co-ordinate triple (East, North, Height) in the output map projection, the corresponding location in the input image is determined through a so-called map-to-image transformation. 2. Subsequently, a grey value is interpolated/resampled from neighbouring input image pixels and assigned to the related output pixel. 3.2.3. Parametric Image-to-lmage Registration Alternatively to geocoding, image data can be registered in image geometry. Based on parametric methods this can be done through a so-called map-to-image transformation of geocoded image data into the geometry of a reference image. In the approach implemented at the Institute of Digital Image Processing, the images to be registered first have to be geocoded using the standard geocoding method. The transformation of the geocoded images to the geometry of another selected reference image is then done in a direct resampling approach in two steps (see Figure 6): 1. For each geocoded input pixel the corresponding output pixel location, i.e. column and line values related to the reference image, is calculated using the transformation from map coordinates to the reference image. Fig. 5. Digital image geocoding workflow. Fig. 6. Workflow of transformation from map to image geometry.

Access restriction

Copyright

fullscreen: Fusion of sensor data, knowledge sources and algorithms for extraction and classification of topographic objects

Monograph

Chapter

Chapter

Table of contents

Cite and reuse

Monograph

Chapter

Image

Image fragment

Citation links

Citation recommendation