Vahid Akbari

A Textural–Contextual Model for Unsupervised Segmentation of Multipolarization Synthetic Aperture Radar Images

This paper proposes a novel unsupervised, non-Gaussian, and contextual segmentation method that combines an advanced statistical distribution with spatial contextual information for multilook polarimetric synthetic aperture radar (PolSAR) data. This extends on previous studies that have shown the added value of both non-Gaussian modeling and contextual smoothing individually or for intensity channels only. The method is based on a Markov random field (MRF) model that integrates a K-Wishart distribution for the PolSAR data statistics conditioned to each image cluster and a Potts model for the spatial context. Specifically, the proposed algorithm is constructed based upon the stochastic expectation maximization (SEM) algorithm. A new formulation of SEM is developed to jointly perform clustering of the data and parameter estimation of the K-Wishart distribution and the MRF model. Experiments on simulated and real PolSAR data demonstrate the added value of using an appropriate statistical representation, in combination with contextual smoothing.

Monitoring Glacier Changes Using Multitemporal Multipolarization SAR Images

This paper presents a processing chain for the change detection of Arctic glaciers from multitemporal multipolarization synthetic aperture radar (SAR) images. We produce terrain-corrected multilook complex 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 the 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 the postclassification 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 C-band ENVISAT ASAR images over the Kongsvegen glacier, Svalbard, is used for demonstration.

Polarimetric SAR Change Detection With the Complex Hotelling–Lawley Trace Statistic

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 (HLT) statistic for measuring the similarity of two covariance matrices. The distribution of the HLT 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 detection rates and error rates that are comparable with the generalized likelihood ratio test.

Improved Ground Subsidence Monitoring Using Small Baseline SAR Interferograms and a Weighted Least Squares Inversion Algorithm

We present the application of a weighted least squares (WLS) method based on image mode interferometric data to monitor the spatiotemporal evolution of land surface subsidence in Mashhad valley, northeast Iran. The technique is based on an appropriate combination of differential interferograms produced by image pairs with small orbital separation to limit the spatial decorrelation phenomena. Our data consist of 17 ASAR single-look-complex images acquired from a descending orbit by the European ENVISAT satellite in image mode (I2), spanning a time interval from June 2004 to November 2007. Fifty-three reliable differential interferograms with relatively little noise and a continuous unwrapped phase are constructed from this data set and are analyzed using a WLS adjustment technique to produce time series of the displacement field. The time-series analysis suggests that the subsidence occurs within a northwest-southeast elongated elliptically shaped bowl along the axis of Mashhad valley. The maximum accumulated subsidence during the 1260-day period reaches approximately 86 cm, located northeast of Mashhad city. The comparison between SAR-interferometry time-series results with continuous Global Positioning System measurements yields an estimated root-mean-square error of ~ 1.0 cm.

The Hotelling-Lawley trace statistic for change detection in polarimetric SAR data under the complex Wishart distribution

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.

Speckle reduction of SAR images using curvelet and wavelet transforms based on spatial features characteristics

Synthetic Aperture Radar (SAR) satellite sensors recently provide valuable sources of earth observation data for various environmental applications. Beside the specifics properties of these data including multi-polarization and polarimetric image data, the presence of unavoidable speckle seriously degrades the quality of these data. Specifically, in certain applications such as clustering, classification and change detection speckles make some difficulties in analysis data and interpretation of results. In this research, a hybrid approach, based on frequency-domain transforms, is proposed. This method is a combination of wavelet and curvelet transforms to suppress the speckle noise in SAR images. This approach based on features and region which has a good efficiency in removing noise and preserving information of data in case of edges and shape. Results of these methods were compared simultaneously and with conventional speckle filtering methods (e.g. Lee, Frost and Kuan).

A K-Wishart Markov random field model for clustering of polarimetric SAR imagery

A clustering method that combines an advanced statistical distribution with spatial contextual information is proposed for multilook polarimetric synthetic aperture radar (PolSAR) data. It is based on a Markov random field (MRF) model that integrates a K-Wishart distribution for the PolSAR data statistics conditioned to each image cluster and a Potts model for the spatial context. Specifically, the proposed algorithm is constructed based upon the expectation maximization (EM) algorithm. A new formulation of EM is developed to jointly address parameter estimation in the K-Wishart distribution and the spatial context model, and also minimization of the energy function. Experiments are presented with simulated and real quad-pol L-band data.

A change detector for polarimetric SAR data based on the relaxed Wishart distribution

In this paper, we present an unsupervised change detection method for polarimetric synthetic aperture radar (Pol-SAR) images based on the relaxed Wishart distribution. Most polarimetric change detectors assume the Gaussian-based complex Wishart model for multilook covariance matrices, which is only satisfied for homogeneous areas with fully developed speckle and no texture. Liu et al. recently proposed a new change detection algorithm under the multilook product model (MPM) to describe the heterogeneous clutters. The improvement has come at the expense of higher computational cost since the similarity measure is based on more advanced models accounting for texture, and they contain some mathematical special functions that is difficult to evaluate such similarity measures. In this paper, we will demonstrate the ability of the relaxed Wishart distribution for textured change detection analysis. Change results on simulated and real data demonstrate the effectiveness of the algorithm.

Iceberg detection in open water and sea ice using C-band radar polarimetry

Icebergs can cause a significant danger for shipping, offshore oil exploration, and undersea or subsea pipelines and production facilities. Synthetic aperture radar (SAR) is very valuable tool of detecting and monitoring icebergs in the often dark and cloud-covered polar regions. Detection of small icebergs floating in nonhomegeous sea clutter environments is a challenging task in remote sensing. In this paper, a new methodology is proposed for automatic identification of icebergs in high-resolution polarimetric SAR images acquired during different seasons. This involves adapting the algorithm to sea-ice conditions, and facing challenges when it comes to high iceberg density, meteorological and oceanographic phenomena in the marginal ice zone causing heterogeneity in the background clutter. The algorithm is tested with time series of RADARSAT-2 C-band quad-polarimetric images to detect icebergs in Kongsfjorden (Ny-Ålesund, Svalbard) that have broken off from the glaciers nearby.

Speckle reduction and restoration of synthetic aperture radar data with an adoptive Markov random field model

This paper proposes a novel speckle reduction method that combines an advanced statistical distribution with spatial contextual information for SAR data. The method for despeckling is based on a Markov random field (MRF) that integrates a K-distribution for the SAR data statistics and a Gauss-MRF model for the spatial context. These two pieces of information are combined based on weighted summation of pixel-wise and contextual models. This not only preserves edge information in the image, but also improves signal-to-noise ratio (SNR) of the despeckled data. Experiments on real SAR data demonstrate the effectiveness of the algorithm compared with well-known despeckling methods.