Abstract-In this paper, we propose a new test statistic for unsupervised change detection in polarimetric radar images. We work with multilook complex covariance matrix data, whose underlying model is assumed to be the scaled complex Wishart distribution. We use the complex-kind Hotelling-Lawley trace statistic for measuring the similarity of two covariance matrices. The distribution of the Hotelling-Lawley trace statistic is approximated by a Fisher-Snedecor distribution, which is used to define the significance level of a false alarm rate regulated change detector. Experiments on simulated and real PolSAR data sets demonstrate that the proposed change detection method gives detections rates and error rates that are comparable with the generalized likelihood ratio test.

This paper presents a processing chain for change detection of Arctic glaciers from multitemporal multipolarization synthetic aperture radar images. We produce terrain corrected multilook complex (MLC) covariance data by including the effects of topography on both geolocation and SAR radiometry as well as azimuth slope variations on polarization signature. An unsupervised contextual non-Gaussian clustering algorithm is employed for segmentation of each terrain corrected polarimetric SAR image and subsequently labeled with the aid of ground truth data into glacier facies. We demonstrate the consistency of the segmentation algorithm by characterizing the expected random error level for different SAR acquisition conditions. This allows us to determine whether an observed variation is statistically significant and therefore can be used for post-classification change detection of Arctic glaciers. Subsequently, the average classified images of succeeding years are compared, and changes are identified as the detected differences in the location of boundaries between glacier facies. In the current analysis, a series of dual polarization Cband ENVISAT ASAR images over the Kongsvegen glacier, Svalbard, is used for demonstration.

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In this paper we propose a new test statistic for unsupervised change detection in polarimetric synthetic aperture radar (Pol-SAR) data. We work with multilook complex (MLC) covariance matrix data, whose underlying model is assumed to be the scaled complex Wishart distribution. We use the complex kind Hotelling-Lawley (HL) trace statistic for measuring the similarity of two covariance matrices. The sampling distribution of the HL trace is approximated by a Fisher-Snedecor distribution, which is used to define the significance level of a constant false alarm rate change detector. The performance of the proposed method is tested on simulated and real PolSAR data sets and compared to the likelihood ratio test statistic.