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Proceedings Paper

Low-fidelity space-based imagery for automatic feature extraction using a multisensor fusion approach under IMaG
Author(s): Shin-Yi Hsu; J. Ching-Yang Huang
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Paper Abstract

Rules for extracting objects and features from remotely sensed data tend to become case specific and thus lack of generalizability beyond the training area. To alleviate the severity of this problem, we propose to low fidelity space- based imagery to extract objects in the context of multisensor fusion. The test site is Sarajevo, and the data sets are LANDSAT TM multispectral and Canadian RADARSAT synthetic aperture radar (SAR) data. The software environment is IMaG system developed by Susquehanna Resources and Environment, Inc. Since IMaG allows one to perform spectral and spatial integration using a scripted programming language, objects existing in two dissimilar sensor domains can be merged and extracted by using soft decision rules that are more generalizable than hard decision rules based on conventional supervised classification methods. Objects extracted in the test site include the built-up area, the runway, rivers, pine forests, and so on.

Paper Details

Date Published: 4 December 1998
PDF: 11 pages
Proc. SPIE 3500, Image and Signal Processing for Remote Sensing IV, (4 December 1998); doi: 10.1117/12.331886
Show Author Affiliations
Shin-Yi Hsu, SUNY/Binghamton (United States)
J. Ching-Yang Huang, Susquehanna Resources and Environment Inc. (United States)

Published in SPIE Proceedings Vol. 3500:
Image and Signal Processing for Remote Sensing IV
Sebastiano Bruno Serpico, Editor(s)

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