1 November 1999 Adaptive image segmentation neural network: application to Landsat images
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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.
© (1999) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Jose L. Alba Castro, Jose L. Alba Castro, Susana M. Rey, Susana M. Rey, Laura Docio, 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); doi: 10.1117/12.367699; https://doi.org/10.1117/12.367699

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