A Theory for Multiresolution Signal Decomposition: The Wavelet Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
The nature of statistical learning theory
The nature of statistical learning theory
Optical and sonar image classification: wavelet packet transform vs Fourier transform
Computer Vision and Image Understanding - Special issue on underwater computer vision and pattern recognition
Pattern Recognition and Image Preprocessing
Pattern Recognition and Image Preprocessing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Texture segmentation using wavelet transform
Pattern Recognition Letters
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Mathematical Theory of Communication
A Mathematical Theory of Communication
A Wrapper for Feature Selection Based on Mutual Information
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 02
Remote Sensing Digital Image Analysis: An Introduction
Remote Sensing Digital Image Analysis: An Introduction
Feature selection with dynamic mutual information
Pattern Recognition
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Input feature selection for classification problems
IEEE Transactions on Neural Networks
Using mutual information for selecting features in supervised neural net learning
IEEE Transactions on Neural Networks
Estimating redundancy information of selected features in multi-dimensional pattern classification
Pattern Recognition Letters
RETRACTED: Application of Bayes linear discriminant functions in image classification
Pattern Recognition Letters
Low bias histogram-based estimation of mutual information for feature selection
Pattern Recognition Letters
Computers and Electrical Engineering
Mutual information-based method for selecting informative feature sets
Pattern Recognition
Computational Intelligence and Neuroscience
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Textural features play increasingly an important role in remotely sensed images classification and many pattern recognition applications. However, the selection of informative ones with highly discriminatory ability to improve the classification accuracy is still one of the well-known problems in remote sensing. In this paper, we propose a new method based on the Gaussian mixture model (GMM) in calculating Shannon's mutual information between multiple features and the output class labels. We apply this, in a real context, to a textural feature selection algorithm for multispectral image classification so as to produce digital thematic maps for cartography exploitation. The input candidate features are extracted from an HRV-XS SPOT image of a forest area in Rabat, Morocco, using wavelet packet transform (WPT) and the gray level cooccurrence matrix (GLCM). The retained classifier is the support vectors machine (SVM). The results show that the selected textural features, using our proposed method, make the largest contribution to improve the classification accuracy than the selected ones by mutual information between individual variables. The use of spectral information only leads to poor performances.