Estimating posterior probabilities for terrain classification with a softmax-based neural network

Alicia Guerrero-Curieses; Jesus Cid-Sueiro

doi:10.1117/12.413906

19 January 2001 Estimating posterior probabilities for terrain classification with a softmax-based neural network

Alicia Guerrero-Curieses, Jesus Cid-Sueiro

Proceedings Volume 4170, Image and Signal Processing for Remote Sensing VI; (2001) https://doi.org/10.1117/12.413906
Event: Europto Remote Sensing, 2000, Barcelona, Spain

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.

Citation Download Citation

Alicia Guerrero-Curieses and Jesus Cid-Sueiro "Estimating posterior probabilities for terrain classification with a softmax-based neural network", Proc. SPIE 4170, Image and Signal Processing for Remote Sensing VI, (19 January 2001); https://doi.org/10.1117/12.413906

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