Citation
Shao, Yang; Taff, Gregory N.; & Walsh, Stephen J. (2011). Comparison of Early Stopping Criteria for Neural-Network-Based Subpixel Classification. IEEE Geoscience and Remote Sensing Letters, 8(1), 113-117.Abstract
A neural-network-based subpixel classification is one of the most commonly used approaches to address spectral mixture problems. Neural-network subpixel-classification performance is directly related to the network-training protocols used. This letter examined early stopping criteria for network training of subpixel land-cover classification. A new stopping criterion is proposed that is based on the reduction of mean squared error (MSE) for a validation data set. We obtained excellent results by stopping the network training when the reduction of MSE between training iterations became marginal. Furthermore, the neural-network learning rate can be used as a threshold value to identify the stopping point. The approach appeared to be robust for both simulation data and actual remote-sensing data. Use of this criterion outperformed two other commonly used stopping criteria: a predefined number of training iterations and a cross-validation approach.URL
http://dx.doi.org/10.1109/LGRS.2010.2052782Reference Type
Journal ArticleYear Published
2011Journal Title
IEEE Geoscience and Remote Sensing LettersAuthor(s)
Shao, YangTaff, Gregory N.
Walsh, Stephen J.