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Proceedings Paper

Neural net spectral pattern recognition
Author(s): Jorge V. Geaga; Thach C. Le; Charlene T. Sailer
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Paper Abstract

Present remote sensing systems are capable of producing digital image data at rates which far exceed the exploitation capabilities of existing processing systems. Automated image classification and interpretation tools are necessary to optimize the use of remotely sensed multispectral imagery. We have investigated the use of artificial neural networks (ANN) for spectral pattern recognition in multispectral imagery for both polarimetric synthetic aperture radar (SAR) and Landsat Thematic Mapper (TM) data. We have used ANN to segment SAR and TM scenes into a few broad land use/land cover (LU/LC) types (e.g., vegetation, bare soil, water, etc.). We believe that these broad landuse classes can be subclassified further into more refined types (e.g., vegetation, class can be partitioned into different vegetation types) using spectral information, spatial shape indicators, and contextual image information such as texture.

Paper Details

Date Published: 16 December 1992
PDF: 10 pages
Proc. SPIE 1766, Neural and Stochastic Methods in Image and Signal Processing, (16 December 1992); doi: 10.1117/12.130865
Show Author Affiliations
Jorge V. Geaga, The Aerospace Corp. (United States)
Thach C. Le, The Aerospace Corp. (United States)
Charlene T. Sailer, The Aerospace Corp. (United States)

Published in SPIE Proceedings Vol. 1766:
Neural and Stochastic Methods in Image and Signal Processing
Su-Shing Chen, Editor(s)

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