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Proceedings Paper

Estimating posterior probabilities for terrain classification with a softmax-based neural network
Author(s): Alicia Guerrero-Curieses; Jesus Cid-Sueiro
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Paper Abstract

The problem of identifying terrains in Landsat-TM images on the basis of non-uniformly distributed labeled data is discussed in this paper. Our approach is based on the use of neural network classifiers that learn to predict posterior class probabilities. Principal Component Analysis (PCA) is used to extract features from spectral and contextual information. The proposed scheme obtains lower error rates that other model-based approaches.

Paper Details

Date Published: 19 January 2001
PDF: 7 pages
Proc. SPIE 4170, Image and Signal Processing for Remote Sensing VI, (19 January 2001); doi: 10.1117/12.413906
Show Author Affiliations
Alicia Guerrero-Curieses, Univ. Carlos III de Madrid (Spain)
Jesus Cid-Sueiro, Univ. Carlos III de Madrid (Spain)


Published in SPIE Proceedings Vol. 4170:
Image and Signal Processing for Remote Sensing VI
Sebastiano Bruno Serpico, Editor(s)

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