## 1 Introduction  
Change detection is the process of identifying the differences on the Earth's surface by multi-temporal images acquired in the same geographical area at different times [1; 2]. During recent years, because of the advantage of repetitive data acquisition, both satellite images and aerial photographs obtained by optical and synthetic aperture radar (SAR) sensors have been widely used in land-cover change detection. The change information is important because of its practical use in applications such as land-use and land-cover dynamic analysis [3; 4] and environmental monitoring [5; 6]. Although optical images have been widely applied in change detection [2; 3; 7], night-time and severe weather limit the use of optical images in practice. Because SAR is an active microwave sensor with all-weather, day-and-night operational imaging capability [8], the use of SAR sensors instead of optical sensors is attractive in change detection studies [9]. Recently, many researchers have focusedon change detection and time-series analysis based on SAR images [10; 11; 12]. Several methods have been designed for single-channel SAR images [13; 14; 15; 16], but the interpretation of the backscattering changes in land cover is limited [6]. Compared with single-channel SAR images, polarimetric SAR (PolSAR) sensors acquire phase and amplitude information in different polarizations, and thus offer more scattering information [17; 18]. However, literature on change detection with PolSAR data is sparse [9]. Because of the potential to provide improved detection capability and to extend the application of PolSAR data in change detection, we explore multi-temporal PolSAR data for change detection in this study. Change detection based on multi-temporal PolSAR images can be classified into two categories: supervised and unsupervised [19]. Although the supervised method, such as the widely used post-classification comparison (PCC), can provide more information on both the change extent and the type of change [18], it can be easily biased by the cumulative error caused by the classification of individual remote sensing image during change detection processing. Moreover, the supervised change detection technique depends on the classification accuracy of multi-temporal PolSAR imagery, which can be easily affected by manual intervention (such as the training sample selection). Compared with the supervised method, the unsupervised change detection approach is relatively straightforward, easy to implement and interpret without human intervention. Due to these reasons, this paper focuses on change detection based on an unsupervised method. In general, unsupervised change detection in SAR images includes three steps: (1) preprocessing, (2) generating the comparison image (CI), and (3) making a decision based on the analysis of the CI in order to obtain the binary change map [9; 18]. The preprocessing of multi-temporal SAR images is a critical step in the change detection and involves radiometric calibration, image co-registration, and speckle filtering. Radiometric calibration is necessary for the comparison of SAR images acquired at different dates. After radiometric calibration, the pixel values of multi-temporal SAR images are related to the radar backscattering. Image co-registration [20; 21] is aimed at reducing the errors caused by the misregistration of the images and aligns the images used in the change detection as precisely as possible. Speckle noise needs to be suppressed by speckle filtering [22] before change detection. In order to obtain a good result in unsupervised change detection, the second and third steps play an important role in processing. The change detection result depends on both the quality of the CI (Step 2) and the quality of the threshold choice (Step 3) [11; 14]. In order to obtain the CI in the second step, there are many different approaches that can be used to compare the preprocessed images of the same geographical area at different dates.