A new PCA-based method for data compression and enhancement of multi-frequency polarimetric SAR imagery
作者： Salim Chitroub , Amrane Houacine , Boualem Sansal
关键词: Principal component analysis 、 Covariance 、 Pattern recognition 、 Mathematics 、 Diagonal matrix 、 Covariance matrix 、 Orthogonal matrix 、 Identity matrix 、 Noise (signal processing) 、 Multiplicative noise 、 Artificial intelligence
摘要: A new PCA-based method for an optimal representation of multi-frequency polarimetric SAR images is proposed. The performs the simultaneous diagonalization signal and multiplicative noise covariance matrices via one orthogonal matrix. matrix becomes identity matrix, which implies that variance in each image unity, uncorrelated between transformed images. to a diagonal whose elements are ordered decreasing value, means will be by their variances (qualities). theoretical analysis implementation procedure given. has been applied on real compression ability proved reconstitution process original from small number with minimal loss information.
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