Synthetic Aperture Radar (SAR) data have gained interest for a variety of remote sensing applications, given the capability of SAR sensors to operate independent of solar radiation and day/night conditions. However, the radiometric quality of SAR images is hindered by speckle noise, which affects further image processing and interpretation. As such, speckle reduction is a crucial pre-processing step in many remote sensing studies based on SAR imagery. This study proposes a new adaptive de-speckling method based on a Gaussian Markov Random Field (GMRF) model. The proposed method integrates both pixel-wised and contextual information using a weighted summation technique. As a by-product of the proposed method, a de-speckled pseudo-span image, which is obtained from the least-squares analysis of the de-speckled multi-polarization channels, is also produced. Experimental results from the medium resolution, fully polarimetric L-band ALOS PALSAR data demonstrate the effectiveness of the proposed algorithm compared to other well-known de-speckling approaches. The de-speckled images produced by the proposed method maintainthe mean value of the original image in homogenous areas, while preserving the edges of features in heterogeneous regions. In particular, the equivalent number of look (ENL) achieved using the proposed method improves by about 15% and 47% compared to the NL-SAR and SARBM3D de-speckling approaches, respectively. Other evaluation indices, such as the mean and variance of the ratio image also reveal the superiority of the proposed method relative to other de-speckling approaches examined in this study.
Increased discrimination capability provided by polarimetric synthetic aperture radar (PolSAR) sensors compared to single and dual polarization synthetic aperture radar (SAR) sensors can improve land use monitoring and change detection. This necessitates reliable change detection methods in multitemporal PolSAR datasets. This paper proposes an unsupervised change detection algorithm for multilook PolSAR data. In the first step of the method, the Hotelling-Lawley trace (HLT) statistic is applied to measure the similarity of two multilook covariance matrices. As a result of this step, a scalar test statistic image is generated. Then, in the second step, a generalized Kittler and Illingworth (K&I) minimum-error thresholding algorithm is developed to perform on the test statistic image and discriminate between changed and unchanged areas. The K&I thresholding algorithm is based on the generalized Gamma distribution for statistical modeling of change and no-change classes. The proposed methodology is tested on a simulated PolSAR data and two C-band fully PolSAR datasets acquired by the uninhabited aerial vehicle SAR and RADARSAT-2 SAR satellites. The experiments show that the proposed algorithm accurately discriminates between change and no-change areas providing detection results with noticeably lower error rates and higher detection accuracy values compared to those of a CFAR-type thresholding of the HLT statistic. Also, the performance of the HLT statistic compared to the other statistics applied on the multilook polarimetric SAR data is assessed. Goodness-of-fit test results prove that the estimated generalized Gamma class conditional models adequately fit the corresponding change and no-change classes.