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30 December 1994 Multisource SAR image texture classification using an artificial neural network model
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SAR image classification work is completely different with optical image work. Information comes from electro-magnetics property of soil. We characterize all kind of soil with texture. This notion depends on noise found from pixel information. Rules and principles of inherence from different kind of soil are not known. We use neural network to build them through example data. After few trials, we select 'variogramme'encoding to describe local fluctuations of pixel values from analyse window. Single information has achieved its limits. We must find another source of information to divide nearest texture classes. We study different architectures of treatment to fit the methodology to the found difficulties to divide classes. Image filtering change strongly textural information found from each class. We hope increase number of information source from the application of different kind of filter on rough image. We analyse the rough image (hole resolution) from (5x5) size analyse window or (11x11) size analyse window and the damaged resolution (by factor 2) image from (5x5) size analyse window. To improve results, we develop noise reducer filters and we elaborate non correlative data. We search best architecture of network and choose hierarchical organization of learning. So, we test the approach with hole image.
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Philippe Mainguenaud and Robert Jeansoulin "Multisource SAR image texture classification using an artificial neural network model", Proc. SPIE 2315, Image and Signal Processing for Remote Sensing, (30 December 1994);


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