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Complementary information contained in each of the individual polarization mode of fully polarimetric PALSAR data is fused using Expectation Maximization (EM) algorithm to improve information contents in the fused image. A comparative analysis of supervised classifiers viz. parallelepiped and minimum distance is accomplished to assess the suitability of the particular classifier. It is also demonstrated...
In this paper, an attempt has been made to improve information of an image by fusion technique, so it may be more effectively used. Full polarimetric images of ALOS PALSAR are fused by exploiting the nice properties of Expectation Maximization (EM) algorithm. Maximum likelihood classifier is applied on the composite unfused singlet and fused doublet images and results are compared on the basis of...
Present paper deals the fusion of image analysis with electromagnetic and support vector machine (SVM) optimization approach to estimate the depth of shallow buried metallic and dummy mine (i.e., without explosive) objects with microwave remote sensing data at X-band (i.e., 10 GHz). The objects were buried under dry and smooth sand. For this purpose, a monostatic scatterometer at X-band has been indigenously...
With the availability of multisensor and multiresolution image data from operational Earth Observation satellites, the fusion of digital image data has become a valuable tool in land cover classification. Digital image fusion is a relatively new research field at the leading edge of available technology. It forms a rapidly developing area of research in land cover classification. It is needed that...
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