Statistical modeling is essential to SAR (Synthetic Aperture Radar) image interpretation. It aims to describe SAR images through statistical methods and reveal the characteristics of these images. Moreover, statistical modeling can provide a technical support for a comprehensive understanding of terrain scattering mechanism, which helps to develop algorithms for effective image interpretation and creditable image simulation. Numerous statistical models have been developed to describe SAR image data, and the purpose of this paper is to categorize and evaluate these models. We first summarize the development history and the current researching state of statistical modeling, then different SAR image models developed from the product model are mainly discussed in detail. Relevant issues are also discussed. Several promising directions for future research are concluded at last.

In the detection applications of synthetic aperture radar (SAR) data, a crucial problem is developing precise models for clutter statistics. Generalized gamma distribution (GΓD) has been widely applied in many fields of signal processing, and it has been demonstrated to be an appropriate model for describing the statistical behaviors of SAR sea clutter, wherein parameter estimation is a key issue for determining the practical application of GΓD. Work that contains three major aspects is performed in this paper. First, an approximate estimator for GΓD parameters based on the well-known “method-of-log-cumulants” is derived; a theoretical comparison between the approximate estimator and other known estimators is also presented. Second, based on this estimator, a scheme of parameter estimation is further given by comprehensively considering estimation precision, speed, and applicable conditions. The simulation results show that the presented scheme is fast and effective. Third, we assess the fitting performance of GΓD and the proposed scheme using real SAR sea clutter data, and compare the model with generalized-K distribution. The experiments on single-look complex and multilook processing L-band ALOS-PALSAR and C-band RADARSAT-2 SAR data verify the effectiveness of the proposed scheme of GΓD parameter estimation. Moreover, several examples of ship detection in real SAR images testify to the usefulness of the proposed scheme in practical applications.

Synthetic aperture radar (SAR) ship detection is an important research topic in the field of maritime applications. The geometrical perturbation-polarimetric notch filter (GP-PNF) was recently proposed to be a promising tool and its usefulness in exploiting polarimetric SAR information for ship detection was demonstrated. The work in this paper is devoted to developing a statistical model of the filter in nonhomogeneous sea clutter to achieve constant false alarm rate (CFAR) detection based on the model. First, within the framework of a multiplicative model, the reciprocal of the gamma distribution is used to describe the texture component of sea clutter in nonhomogeneous background. As a result, a statistical model of the GP-PNF is analytically derived and found suitable for sea clutter scenes with a wide range of homogeneity. Second, we theoretically demonstrate that CFAR detection using GP-PNF is unrelated to the parameter in the original GP-PNF. Therefore, a simplified version of the GP-PNF is given. Third, the CFAR threshold of the simplified filter is mathematically derived. Experiments performed on measured L-band ALOS-PALSAR and C-band RADARSAT-2 SAR data verify the good performance of the developed statistical model and demonstrate the usefulness of the CFAR detection based on the simplified filter.

Ship detection and classification pose critical challenges in maritime surveillance and monitoring. On the one hand, they find applications in civilian domains, like fishery management, illegal immigrant monitoring, and marine rescue. On the other hand, ships are important targets in maritime combat. Ship detection and classification help to obtain timely maritime military intelligence that holds immense importance in seizing control of the maritime battlefield and ensuring the success of military operations [1], [2]. Remote sensing technology, facilitated by sensors mounted on platforms like satellites and airplanes, enables long-distance noncontact imaging of ground targets. It offers high timeliness, wide coverage, and low-cost accessibility and is not constrained by geographic conditions. Consequently, it has emerged as the primary method for monitoring vessels [4], [5].

Polarimetric whitening filter (PWF) can be used to filter polarimetric synthetic aperture radar (PolSAR) images to improve the contrast between ships and sea clutter background. For this reason, the output of the filter can be used to detect ships. This paper deals with the setting of the threshold over PolSAR images filtered by the PWF. Two parameter-constant false alarm rate (2P-CFAR) is a common detection method used on whitened polarimetric images. It assumes that the probability density function (PDF) of the filtered image intensity is characterized by a log-normal distribution. However, this assumption does not always hold. In this paper, we propose a systemic analytical framework for CFAR algorithms based on PWF or multi-look PWF (MPWF). The framework covers the entire log-cumulants space in terms of the textural distributions in the product model, including the constant, gamma, inverse gamma, Fisher, beta, inverse beta, and generalized gamma distributions (GΓDs). We derive the analytical forms of the PDF for each of the textural distributions and the probability of false alarm (PFA). Finally, the threshold is derived by fixing the false alarm rate (FAR). Experimental results using both the simulated and real data demonstrate that the derived expressions and CFAR algorithms are valid and robust.

Onboard information fusion for multisatellites, which is based on spatial computing mode, can improve the satellites’ capability, such as the spatial–temporal coverage, detection accuracy, recognition confidence, position precision, and prediction precision for disaster monitoring, maritime surveillance, and other emergent or continuous persistent observing situations. First, we analyze the necessity of onboard information fusion. Next, the recent onboard processing developments are summarized and the existing problems are discussed. Furthermore, the key technologies and concepts of onboard information fusion are summarized in the fields of feature representation, association, feature-level fusion, spatial computing architecture, and other issues. Finally, the future developments of onboard information fusion are investigated and discussed.