Estimating Posterior Probabilities for Terrain Classification with a Softwax-based Neural Network

摘要

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 ueural network classifiers that learn to predict posterior class probabilities. Principal Component Analysis (PCA) is used to extract features from spectral and contextual inforamtion. The proposed scheme obtains lower error rates that other model-based approaches.