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Proceedings Paper

Multisensor data fusion for automated scene interpretation
Author(s): Olaf Hellwich; Christian Wiedemann
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Paper Abstract

An approach to the combined extraction of linear as well as two-dimensional objects from multisensor data based on a feature- and object-level fusion of the results is proposed. The data sources are DAIS hyperspectral data, AES-1 SAR data, and high- resolution panchromatic digital orthoimages. Rural test areas consisting of a road network, agricultural fields, and small villages were investigated. The scene interpretation is based on a conceptual model consisting of a semantic net for each of the sensors and a semantic net of the real world objects. The sensor nets and the object net are combined into one network by means of a geometry and material level of network nodes. Road networks are extracted from the panchromatic orthoimage and from selected hyperspectral bands. Based on the knowledge that roads compose networks the extraction results are combined. Two-dimensional, i.e. areal, objects are extracted from hyperspectral data after a principal component transformation. The SAR data is segmented using image intensity and interferometric elevation. The classifications of the hyperspectral and SAR data are combined with the extracted road network using rule- and segment-based methods. In the outlook, comments are given on the trade-off between the improvement of the results using the new method and the increasing costs for data acquisition.

Paper Details

Date Published: 14 December 1999
PDF: 12 pages
Proc. SPIE 3871, Image and Signal Processing for Remote Sensing V, (14 December 1999); doi: 10.1117/12.373266
Show Author Affiliations
Olaf Hellwich, Technische Univ. Muenchen (Germany)
Christian Wiedemann, Technische Univ. Muenchen (Germany)


Published in SPIE Proceedings Vol. 3871:
Image and Signal Processing for Remote Sensing V
Sebastiano Bruno Serpico, Editor(s)

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