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This paper presents a new method for unsupervised terrain classification using fully polarimetric synthetic aperture radar image based on quad-tree segment and support vector machine techniques. This unsupervised classification method begins with quad-tree segment technique that ensures each segment contains the data of only one cluster. Then, the feature vectors are constructed by sampling those...
In this paper, a new scheme for iterative classification of polarimetric SAR image based on the Freeman decomposition and scattering entropy is proposed. This technique extracts three decomposition coefficients of three scattering mechanism components through the Freeman decomposition and the scattering entropy through the H/Alpha decomposition first; then classifies the polarimetric SAR image into...
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