Adaptive image segmentation neural network: application to Landsat images

Jose L. Alba Castro; Susana M. Rey; Laura Docio

doi:10.1117/12.367699

1 November 1999 Adaptive image segmentation neural network: application to Landsat images

Jose L. Alba Castro, Susana M. Rey, Laura Docio

Proceedings Volume 3812, Applications and Science of Neural Networks, Fuzzy Systems, and Evolutionary Computation II; (1999) https://doi.org/10.1117/12.367699
Event: SPIE's International Symposium on Optical Science, Engineering, and Instrumentation, 1999, Denver, CO, United States

Abstract

In this paper we introduce an adaptive image segmentation neural network based on a Gaussian mixture classifier that is able to accommodate unlabeled data in the training process to improve generalization when labeled data is insufficient. The classifier is trained by maximizing the joint-likelihood of features and labels over all the data set (labeled and unlabeled). The classifier builds grey- level images with estimation of class-posteriors (as many images as classes) that feed the segmentation algorithm. The paper is focused on the adaptive classification part of the algorithm. The classification tests are performed over Landsat TM mini-scenes. We assess the efficiency of the adaptive classifier depending on the model complexity and the proportion of labeled/unlabeled data.

Citation Download Citation

Jose L. Alba Castro, Susana M. Rey, and Laura Docio "Adaptive image segmentation neural network: application to Landsat images", Proc. SPIE 3812, Applications and Science of Neural Networks, Fuzzy Systems, and Evolutionary Computation II, (1 November 1999); https://doi.org/10.1117/12.367699

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