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A new feature extraction approach is proposed in this paper to improve the classification performance in remotely sensed data. The proposed method is based on a primary sources subset (PSS) obtained by nonlinear transform that provides lower space for land pattern recognition. First, the underlying sources are approximated using multilayer neural networks. Given that, Bayesian inferences update unknown sources’ knowledge and model parameters with information’s data. Then, a source dimension minimizing technique is adopted to provide more efficient land cover description. The support vector machine (SVM) scheme is developed by using feature extraction. The experimental results on real multispectral imagery demonstrates that the proposed approach ensures efficient feature extraction by using several descriptors for texture identification and multiscale analysis. In a pixel based approach, the reduced PSS space improved the overall classification accuracy by 13% and reaches 82%. Using texture and multi resolution descriptors, the overall accuracy is 75.87% for the original observations, while using the reduced source space the overall accuracy reaches 81.67% when using jointly wavelet and Gabor transform and 86.67% when using Gabor transform. Thus, the source space enhanced the feature extraction process and allow more land use discrimination than the multispectral observations.

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In this paper, we consider the problem of b lind image separation by taking advantage of the sparse representation of the hyperspectral images in the DCT-domain. Blind Source Separation (BSS) is an important field of research in signal and image processing. These images are produced by sensors which provide hundreds of narrow and adjacent spectral bands. The idea behind t ransform do main is that we can restructure the signal/image values to give transform coefficients more easily to separate. This work describes a novel approach based on Second-Order Separat ion by Frequency-Decomposition, termed SOSFD. This technique uses joint information fro m second-order statistics and sparseness decomposition. Furthermore, the proposed approach has the added advantages of the DCT and second-order statistics in order to select the optimu m data informat ion. In fact, representing the hyperspectral images in well suited database functions allows a good distinction of various types of objects. Results show the contribution of this new approach for the hyperspectral image analysis and prove the performance of the SOSFD algorith m for hyperspectral image classification. On the opposite of the original images that are represented according to correlated axes, the source images extracted fro m the proposed approach are represented according to mutually independent axes that allow a more efficient representation of information contained in each image. Then, each source can represent specifically certain themes by exploit ing the link between the frequency-distribution and structural composition of the image. Th is application is of utmost importance in the classification process and could increase the reliability of the analysis and the interpretation of the hyperspectral images.