At present, one of the major strategies of SAR image interpretati

At present, one of the major strategies of SAR image interpretation is to use the methods of classical statistical selleck chemicals pattern recognition, Inhibitors,Modulators,Libraries which are based on Bayesian Theory and can reach a theoretically optimal solution [1,2]. To utilize these methods for SAR image Inhibitors,Modulators,Libraries interpretation, a proper statistical distribution must be adopted to model SAR image data [1,2]. Therefore, in the past ten years, statistical modeling of SAR image has become an active research field [1].Statistical modeling is of great value in SAR image applications. Firstly, it leads to an in-depth comprehension of terrain scattering mechanism. Secondly, it can guide the researches of speckle suppression [3�C9], edge detection [10], segmentation [1,11�C13], classification [14�C17], target detection and recognition [14,18�C20] for SAR images, etc.

Finally, combining statistical model with ISAR target database can simulate various SAR images with variable parameters of aspect, terrain content, region position and SCR (signal to clutter ratio), so statistical modeling can provide numerous data for developing robust algorithms of SAR image interpretation [21].The research on statistical modeling of SAR images may be traced back Inhibitors,Modulators,Libraries to the 1970s. With the acquisition of the first SAR image in the U.S., the analysis of real SAR data directly promoted the development of statistical modeling techniques. The speckle model of SAR images, proposed by Arsenault [22] in 1976, is the origin of these techniques, which established the theoretical foundation of the later researches.

In 1981, Ward [23] presented the product model of Inhibitors,Modulators,Libraries SAR images, which took the speckle model as a special case. As a landmark of the development of statistical modeling, the product model simplified the analysis of modeling. Since then, many scholars joined this research field and many statistical models of SAR images had been developed.Since the 1990s, with the coming forth of a series of air-borne or space-borne SAR platforms, the acquisition of SAR data is no longer a problem. Due to the urgent demands for analyzing and interpreting the obtained image data, statistical modeling has drawn much attention.In recent years, many famous research organizations have been studying SAR statistical modeling [24], and great progress has been made.

According to the collected literatures, from 1986 to 2004, there were more than 100 papers dealing with SAR statistical modeling published in some famous journals such as IEEE-AES, IEEE-IP, IEEE-GRS, and IEE, etc. and at some international conferences such as SPIE and IGARSS. The related papers, which use SAR statistical model for Dacomitinib the purpose of segmentation, selleck chemical speckle suppression, classification and target detection and recognition, are uncountable. Much creative research has been made. Professor Oliver, an English scholar, published his monograph Understanding Synthetic Aperture Radar Images in 1998 [1].

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