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Proceedings Paper

Integration of GIS and remote sensing image analysis techniques
Author(s): Paul C. Smits; Alessandro Annoni; Silvana G. Dellepiane
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Paper Abstract

Classical image analysis techniques have proven to be powerful tools in various remote-sensing image interpretation problems. However, applied to large images their usefulness is limited as the spatial complexity of classes used in land-cover databases often exceeds the identification capability of the methods. Moreover, atmospheric and soil conditions introduce a substantial within-class variability. Land-cover/land-use databases can contain 40 or more different categories which cannot all be derived directly from the image data. The robust integration of GIS and remote sensing image interpretation techniques is important, but is feasible only when both possibilities and limitations are considered. In this paper, the design and implementation is described of a tool for the updating of land-cover polygons by remote-sensing imagery. After a preliminary analysis of the neighboring polygons (i.e., background) around a polygon to update (i.e., object), the best feature is selected out of a set of more than 30 features based on its ability to separate object from background. This best feature is used in a successive image- labeling step. The labeling step adopted in this paper is based on a fuzzy intensity connectedness measure.

Paper Details

Date Published: 14 December 1999
PDF: 8 pages
Proc. SPIE 3871, Image and Signal Processing for Remote Sensing V, (14 December 1999); doi: 10.1117/12.373265
Show Author Affiliations
Paul C. Smits, European Commission Joint Research Ctr. (Italy)
Alessandro Annoni, European Commission Joint Research Ctr. (Italy)
Silvana G. Dellepiane, Univ. degli Studi di Genoa (Italy)


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

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