  Images obtained from coherent illumination processes are contaminated with
speckle. A prominent example of such imagery systems is the polarimetric
synthetic aperture radar (PolSAR). For such remote sensing tool the speckle
interference pattern appears in the form of a positive definite Hermitian
matrix, which requires specialized models and makes change detection a hard
task. The scaled complex Wishart distribution is a widely used model for PolSAR
images. Such distribution is defined by two parameters: the number of looks and
the complex covariance matrix. The last parameter contains all the necessary
information to characterize the backscattered data and, thus, identifying
changes in a sequence of images can be formulated as a problem of verifying
whether the complex covariance matrices differ at two or more takes. This paper
proposes a comparison between a classical change detection method based on the
likelihood ratio and three statistical methods that depend on
information-theoretic measures: the Kullback-Leibler distance and two
entropies. The performance of these four tests was quantified in terms of their
sample test powers and sizes using simulated data. The tests are then applied
to actual PolSAR data. The results provide evidence that tests based on
entropies may outperform those based on the Kullback-Leibler distance and
likelihood ratio statistics.
