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Perceptron Nonlinear Blind Source Separation for Feature Extraction and Image Classification
Conference proceeding

Perceptron Nonlinear Blind Source Separation for Feature Extraction and Image Classification

Mohamed Rached Boussema, Mohamed Saber Naceur and Hela Elmannai
2012 3RD INTERNATIONAL CONFERENCE ON IMAGE PROCESSING THEORY, TOOLS AND APPLICATIONS, pp.259-262
International Conference on Image Processing Theory Tools and Applications
01/01/2012
DOI: https://doi.org/10.1109/IPTA.2012.6469537

Abstract

Engineering Engineering, Electrical & Electronic Imaging Science & Photographic Technology Science & Technology Technology
In this paper, we aim to classify remotely sensed images for land characterisation. The major goal is approaching the natural nonlinear mixture for band observation and then dimension reduction by supervised classification. After that, an unsupervised method combining feature extraction and SVM in investigating to discriminate the land cover for SPOT 4 satellite image. In this technique, training data base are wavelet features that are extracted from a subset of sources.

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