Semisupervised Classification of SAR Images by Maximum Margin Neural Networks Method

The proposed method is based on neural networks by modeling the data marginal distribution with the graph Laplacian built with both labeled and unlabeled samples, at the same time, optimizing neural networks layers in a single process, back-propagating the gradient of a Maximum Margin based objective function. Therefore, the proposed approach gives rise to an operational classifier, as opposed to previously presented semi-supervised scenarios. Results demonstrate the improved classification accuracy and scalability of this approach on SAR image classification problems.

[1]  Urbano Nunes,et al.  Novel Maximum-Margin Training Algorithms for Supervised Neural Networks , 2010, IEEE Transactions on Neural Networks.

[2]  Alexander Zien,et al.  Semi-Supervised Learning , 2006 .

[3]  P. Vasuki,et al.  Man-made object classification in SAR images using Gabor wavelet and neural network classifier , 2012, 2012 International Conference on Devices, Circuits and Systems (ICDCS).

[4]  Shiyong Cui,et al.  Cascade active learning for SAR image annotation , 2012, 2012 IEEE International Geoscience and Remote Sensing Symposium.

[5]  G. F. Hughes,et al.  On the mean accuracy of statistical pattern recognizers , 1968, IEEE Trans. Inf. Theory.

[6]  Mark J. T. Smith,et al.  A SAR Target Classifier Using Radon Transforms and Hidden Markov Models , 2002, Digit. Signal Process..

[7]  Radford M. Neal,et al.  High Dimensional Classification with Bayesian Neural Networks and Dirichlet Diffusion Trees , 2006, Feature Extraction.

[8]  Mikhail Belkin,et al.  Semi-Supervised Learning on Riemannian Manifolds , 2004, Machine Learning.

[9]  Alexander J. Smola,et al.  Learning with Kernels: support vector machines, regularization, optimization, and beyond , 2001, Adaptive computation and machine learning series.

[10]  Bhanu Prasad,et al.  Speech, Audio, Image and Biomedical Signal Processing using Neural Networks , 2008, Studies in Computational Intelligence.

[11]  Pedro E. López-de-Teruel,et al.  Nonlinear kernel-based statistical pattern analysis , 2001, IEEE Trans. Neural Networks.

[12]  Xiao Li,et al.  Maximum margin learning and adaptation of MLP classifiers , 2005, INTERSPEECH.

[13]  Xiaojin Zhu,et al.  --1 CONTENTS , 2006 .

[14]  A. N. Tikhonov,et al.  REGULARIZATION OF INCORRECTLY POSED PROBLEMS , 1963 .