Low-fidelity space-based imagery for automatic feature extraction using a multisensor fusion approach under IMaG

Shin-Yi Hsu; J. Ching-Yang Huang

doi:10.1117/12.331886

4 December 1998 Low-fidelity space-based imagery for automatic feature extraction using a multisensor fusion approach under IMaG

Shin-Yi Hsu, J. Ching-Yang Huang

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Proceedings Volume 3500, Image and Signal Processing for Remote Sensing IV; (1998) https://doi.org/10.1117/12.331886
Event: Remote Sensing, 1998, Barcelona, Spain

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.

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Shin-Yi Hsu and J. Ching-Yang Huang "Low-fidelity space-based imagery for automatic feature extraction using a multisensor fusion approach under IMaG", Proc. SPIE 3500, Image and Signal Processing for Remote Sensing IV, (4 December 1998); https://doi.org/10.1117/12.331886

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KEYWORDS

Radar

Earth observing sensors

Landsat

Sensors

Synthetic aperture radar

Feature extraction

Image fusion

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