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An optimization of the difference of covariance matrices for PolSAR change detection

SAR polarimetry (PolSAR) can play an important role in change detection both in terms of improving the detection capabilities and providing physical information regarding the change. In agricultural context, such information can be used to help retrieving the phenological stage and eventually identifying stress conditions. In this work, a new change detection based on PolSAR data is proposed. The methodology is based on the use of the normalised difference between covariance matrices acquired at two different instants. A diagonalisation of such matrix allows identifying the scattering mechanisms that suffer the largest change. The methodology is tested exploiting L-band quad-polarimetric E-SAR (DLR) data from the AGRISAR 2006 campaign.