Xianxiang Qin

Region-Based Classification of SAR Images Using Kullback–Leibler Distance Between Generalized Gamma Distributions

For the classification of synthetic aperture radar (SAR) images, traditional pixel-based Bayesian classifiers suffer from an intrinsic flaw that categories with serious overlapped probability density functions cannot be well classified. To solve this problem, in this letter, a region-based classifier for SAR images is proposed, where regions, instead of individual pixels, are treated as elements for classification. In the algorithm, each region is assigned to the class that minimizes a criterion referring to the Kullback-Leibler distance. Besides, the generalized gamma distribution (GΓD), a flexible empirical model, is employed for the statistical modeling of SAR images. Finally, with a synthetic image and an actual SAR image acquired by the EMISAR system, the effectiveness of the proposed algorithm is validated, compared with the pixel-based maximum-likelihood method and two region-based Bayesian classifiers.

SAR Image Segmentation via Hierarchical Region Merging and Edge Evolving With Generalized Gamma Distribution

This letter proposes a novel segmentation algorithm for synthetic aperture radar (SAR) images based on hierarchical region merging and edge evolving. To cope with the influence of speckle in SAR images, a statistical stepwise criterion, the loss of log-likelihood function (LLF) of image partition, is utilized for region merging. For this merging procedure, precise distributions of image partitions are essential, and we employ the generalized gamma distribution (GΓD) for modeling SAR images. Besides, the traditional region merging methods often suffer from the initial image partition that may lead to coarse segment shapes. It motivates us introducing a novel edge evolving scheme into the segmentation algorithm. It consists of two iterative steps: the evolution of edge pixels with a maximum likelihood (ML) criterion and that with a maximum a posterior (MAP) criterion using a Markov random field (MRF) model. The performance of the proposed algorithm is validated on two actual SAR images from the AIRSAR and EMISAR systems.

A Fast Superpixel Segmentation Algorithm for PolSAR Images Based on Edge Refinement and Revised Wishart Distance

The superpixel segmentation algorithm, as a preprocessing technique, should show good performance in fast segmentation speed, accurate boundary adherence and homogeneous regularity. A fast superpixel segmentation algorithm by iterative edge refinement (IER) works well on optical images. However, it may generate poor superpixels for Polarimetric synthetic aperture radar (PolSAR) images due to the influence of strong speckle noise and many small-sized or slim regions. To solve these problems, we utilized a fast revised Wishart distance instead of Euclidean distance in the local relabeling of unstable pixels, and initialized unstable pixels as all the pixels substituted for the initial grid edge pixels in the initialization step. Then, postprocessing with the dissimilarity measure is employed to remove the generated small isolated regions as well as to preserve strong point targets. Finally, the superiority of the proposed algorithm is validated with extensive experiments on four simulated and two real-world PolSAR images from Experimental Synthetic Aperture Radar (ESAR) and Airborne Synthetic Aperture Radar (AirSAR) data sets, which demonstrate that the proposed method shows better performance with respect to several commonly used evaluation measures, even with about nine times higher computational efficiency, as well as fine boundary adherence and strong point targets preservation, compared with three state-of-the-art methods.

Simulation of spatially correlated PolSAR images using inverse transform method

Abstract. This paper proposes an algorithm of simulating spatially correlated polarimetric synthetic aperture radar (PolSAR) images based on the inverse transform method (ITM). Three flexible non-Gaussian models are employed as the underlying distributions of PolSAR images, including the KummerU, W and M models. Additionally, the spatial correlation of the texture component is considered, which is described by a parametric model called the anisotropic Gaussian function. In the algorithm, PolSAR images are simulated by multiplying two independent components, the speckle and texture, that are generated separately. There are two main contributions referring to two important aspects of the ITM. First, the inverse cumulative distribution functions of all the considered texture distributions are mathematically derived, including the Fisher, Beta, and inverse Beta models. Second, considering the high computational complexities the implicitly expressed correlation transfer functions of these texture distributions have, we develop an alternative fast scheme for their computation by using piecewise linear functions. The effectiveness of the proposed simulation algorithm is demonstrated with respect to both the probability density function and spatial correlation.

A Likelihood-Based SLIC Superpixel Algorithm for SAR Images Using Generalized Gamma Distribution

The simple linear iterative clustering (SLIC) method is a recently proposed popular superpixel algorithm. However, this method may generate bad superpixels for synthetic aperture radar (SAR) images due to effects of speckle and the large dynamic range of pixel intensity. In this paper, an improved SLIC algorithm for SAR images is proposed. This algorithm exploits the likelihood information of SAR image pixel clusters. Specifically, a local clustering scheme combining intensity similarity with spatial proximity is proposed. Additionally, for post-processing, a local edge-evolving scheme that combines spatial context and likelihood information is introduced as an alternative to the connected components algorithm. To estimate the likelihood information of SAR image clusters, we incorporated a generalized gamma distribution (GГD). Finally, the superiority of the proposed algorithm was validated using both simulated and real-world SAR images.

Statistical modeling of sea clutter in high-resolution SAR images using generalized gamma distribution

Statistical modeling of sea clutter in synthetic aperture radar (SAR) imagery is fundamental for SAR image interpretation. In this paper, we adopt a recently proposed generalized gamma distribution (GrD) for modeling sea clutter in high-resolution SAR images. Based on parameter decoupling, an estimator of GrD, named as scale-independent scale estimation (SISE), is derived, which only refers to several basic operations and can be easily realized. Modeling experiments are carried out over the L-band polarimetric SAR images acquired by JPL/AIRSAR and a VV-polarized C-band TerraSAR-X SAR image. Experimental results show that the advantage of GrD for modeling sea clutter in high-resolution SAR images is evident comparing to the classic distributions of sea clutter in SAR images including the Weibull, Log-normal and K distributions.

Supervised classification of PolSAR images using adaptive sample censoring strategy

In this paper, a supervised classification algorithm of polarimetric synthetic aperture radar (PolSAR) images based on SVM with training sample optimization is proposed. For the supervised strategy, three main steps are there including feature extraction, design of classifier and training of classifier. Firstly, some features of the PolSAR images are extracted by employing the target decomposition theories. Secondly, a strategy for optimizing training sample is applied on each of preliminary selected sample sets, of which an adaptive threshold is determined according to the statistics of the characteristics similarity. Finally, classification of a real PolSAR image is performed based on SVM with the refined training sample sets. The experimental results have proven that the proposed algorithm is able to improve the quality of the samples and also the classification accuracy and the robustness of the classification algorithm for PolSAR images.

A PDF-based SLIC superpixel algorithm for SAR images

The simple linear iterative clustering (SLIC) method is a popular recently proposed superpixel algorithm. However, it may provide bad superpixels for the synthetic aperture radar (SAR) images due to the influence of speckle and large dynamic range of pixel intensity. In this paper, an improved SLIC algorithm for SAR images is proposed by employing the probability density function (PDF) information of SAR image pixel clusters. In this algorithm, a local clustering scheme combining data similarity with spatial proximity is designed, instead of the local k-means clustering used in the standard SLIC method. Moreover, for the post-processing, an edge evolving scheme with a local Bayesian criterion is introduced, instead of the connected components algorithm. In addition, for the precise statistical modeling of SAR images, the generalized gamma distribution (G?D) is exploited. Finally, the superiority of the proposed algorithm is validated on both simulated and real-world SAR images.

A CFAR algorithm for layover and shadow detection in InSAR images based on kernel density estimation

In this paper, a novel CFAR algorithm for detecting layover and shadow areas in Interferometric synthetic aperture radar (InSAR) images is proposed. Firstly, the probability density function (PDF) of the square root amplitude of InSAR image is estimated by the kernel density estimation. Then, a CFAR algorithm combining with the morphological method for detecting both layover and shadow is presented. Finally, the proposed algorithm is evaluated on a real InSAR image obtained by TerraSAR-X system. The experimental results have validated the effectiveness of the proposed method.

A Generalized Gamma Distributed CFAR Algorithm for Layover and Shadow Detection in InSAR Images

In this paper, a novel CFAR algorithm is proposed for layover and shadow detection in Interferometric synthetic aperture radar (InSAR) images. Firstly, the generalized gamma distribution (GГD) is employed for statistical modeling of the InSAR image. Moreover, a CFAR algorithm for detecting both the layover and shadow is proposed, of which the analytical expressions of two thresholds are presented. Finally, the effectiveness of the GГD and the proposed CFAR algorithm are validated with a real InSAR image.