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 mentioned and briefly discussed only for reasons of completeness. Although it is widely acknowledged that such methods do not at all fulfil serious accuracy requirements, they are still applied by many users in their remote sensing applications. Emphasis, however, will be put on methods which allow a precise coregistration or geocoding of images in order to provide optimum datasets for subsequent fusion-based applications. Suitable approaches to precisely fuse data from a geometric point of view are: • parametric methods, being based on the consideration of sensor-specific mapping models following photogrammetric techniques to transform an image to a map or vice versa. • coregistration techniques being independent of any sensor- specific imaging parameters. Here, registration can be performed in image or map geometry based on one and the same approach. Pre-operational and operational image coregistration and geocoding have been the topic of various research activities related to remote sensing and GIS at the Institute of Digital Image Processing. Illustrative examples of these activities are included to show the potential as well as the drawbacks of the various methods being described. 2. DISPLACEMENT EFFECTS FOR MONITORING APPLICATIONS A major restriction for the application of multisensor and multitemporal image data for monitoring applications is the position accuracy when overlaying the different images. In order to demonstrate the necessity of high location accuracy a simulation has been performed to analyse displacement effects for forest monitoring applications. The simulation was carried out by shifting a forest type classification, for which three different forest classes have been derived from geocoded Landsat TM image data by a magnitude of 0.5 pixel each to east and north (25 m pixel footprint). According to many reported Landsat TM geocoding results, 0.5 pixels can be assumed to be the optimal realistic geometric accuracy that can by reached by parametric methods. The classification error resulting from the simulated geocoding error can then be estimated by comparing the shifted classification with the original one. The class changes due to the geocoding error for the three forest types are listed in Table 1. In case of no displacement, or of no effects of the displacement, the change between the classes being compared should be 0%. In other words, the comparisons between e.g. coniferous and coniferous should amount to 100%. In the simulated case, however, only 86.74% of the coniferous pixels still belong to the "coniferous"-class after the displacement. This means that the displacement leads to an error of 13.26%. This error splits up into the coniferous pixels, which are incorrectly displaced into the class "mixed forest" (11.17%) and the coniferous pixels, which are incorrectly displaced into the class "deciduous forest" (2.09%). Table 1. Classification changes caused by a geocoding error of 0.5 pixel to east and north. Changes of coniferous forest coniferous => coniferous 86.74 % coniferous => mixed 11.17 % coniferous => deciduous 2.09 % Changes of deciduous forest deciduous => coniferous 20.18% deciduous => deciduous 69.07 % deciduous => mixed 10.75 % Changes of mixed forest mixed => coniferous 8.63 % mixed => deciduous 16.99% mixed => mixed 74.38 % By shifting the forest type classification by an assumed geocoding error of 1 pixel each to east and north, the values in Table 2 were obtained. Table 2. Classification changes caused by a geocoding error of 1.0 pixel to east and north. Changes of coniferous forest coniferous => coniferous 83.55 % coniferous => mixed 13.96 % coniferous => deciduous 2.49 % Changes of deciduous forest deciduous => coniferous 25.98 % deciduous => deciduous 60.07 % deciduous ==» mixed 13.95 % Changes of mixed forest mixed coniferous 10.00 % mixed => deciduous 22.48 % mixed => mixed 67.52 % These simulations show that a pixel-accuracy comparison of signatures or classification results cannot be recommended for forest monitoring applications, since changes in forest classes occur in many cases in subpixel dimensions. This is particularly true for forest types, which are characterised by heterogeneous spatial distribution. This can be demonstrated by the correspondence values of the class "mixed forest", for which correspondence values of only 74.38% (shift of 0.5 pixel to east and north) and 67.52% (shift of 1.0 pixel to east and north) could be noticed. These classification changes are just one indicator for the necessity of high-accuracy image geocoding. Methods to fulfil this demand are discussed in the following section. 3. COREGISTRATION AND GEOCODING METHODS 3.1. Polynomial Rectification Straightforward polynomial rectification and image warping tools have been used to register and/or rectify image data for

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