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Proceedings Paper

Comparison levels for iterative estimators for model-based recognition of man-made objects in remote-sensing images
Author(s): Klamer Schutte; G. J. Hilhorst
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Paper Abstract

An iterative estimation procedure is applied to the determination of 3-D parameters from Remote Sensing image data. The 3-D parameters describe the geometry of the scene viewed. The scene consists of man-made objects, such as buildings. The estimation procedure is based on a prediction-correction mechanism. The prediction is generated using a model of the imaging process. An essential point in such an estimation algorithm is the comparison level used. The choice of the comparison level is dependent on many factors. The key factors studied are the possible information loss in data reduction, and the introduction of modelling errors in the prediction model. These modelling errors might be smaller on higher data levels. Two comparison levels are explored in depth in this research. The first comparison level studied is the level that describes the shape of regions found by segmentation. The predicted and measured regions are matched using an error measure based upon the regions run-length encoding. The error is minimal if length and position of the scan line segments match. The second level studied is reached by calculating the moments of the regions. The error measure consists of the subtraction of predicted and measured moments. Results show that the proposed approach is possible. The speedup obtained, compared to using the raw data as comparison level, is substantial, typical in the same order as the data reduction applied. The accuracy of the final parameter values is comparable between the tested levels.

Paper Details

Date Published: 10 June 1993
PDF: 7 pages
Proc. SPIE 1904, Image Modeling, (10 June 1993); doi: 10.1117/12.146694
Show Author Affiliations
Klamer Schutte, Univ. of Twente (Netherlands)
G. J. Hilhorst, Univ. of Twente (Netherlands)

Published in SPIE Proceedings Vol. 1904:
Image Modeling
Lawrence A. Ray; James R. Sullivan, Editor(s)

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