Multifrequency Omnibus Change Detection in Covariance Matrix PolSAR Data

In this letter we work with truly multitemporal change detection in multilooked, multifrequency polarimetric synthetic aperture radar (polSAR) data in the covariance matrix formulation. We apply recent general results on better approximations than the usual chi-squared distribution for the probability distributions associated with maximum likelihood ratio test statistics for equality of several block-diagonal covariance matrices with complex Wishart distributed blocks. We demonstrate the superiority of the new approximations by means of generated data and airborne EMISAR data from four time points covering an agricultural region in Denmark. Results from the generated data show the importance of applying the new approximations in the no change situation. This use is more important for low equivalent number of looks (ENL) and for long time series (i.e., high number of degrees of freedom). Results from the generated data example are confirmed by results from the case with EMISAR data.