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In the problem of aircraft detection for High resolution (HR) Synthetic aperture radar (SAR) images, the background areas commonly contain multiple land cover types, such as runways and grassland. The conventional Constant false alarm rate (CFAR) detection in these non-homogeneous backgrounds with homogeneous assumption leads to unreliable detection results. This paper constructs a one-stage detection method based on the Generalized gamma mixture distribution (GGMD), which is regarded as a competitive and applicable model for combining the advantages of the Generalized gamma distribution (GGD) and the Finite mixture model (FMM). In order to evaluate the availability of the proposed algorithm, HR SAR images for aircraft detection from different product types and with various resolutions are examined. Compared with the CFAR algorithms based on the Gamma distribution, the GGD, and the gamma mixture distribution, the proposed algorithm demonstrates its availability and effectiveness for aircraft detection in HR SAR images in non-homogeneous background.

A novel physical paradigm is explored in this paper: synthetic-aperture radar (SAR) ocean speckle is informative. This paper experimentally analyzes the SAR ocean speckle intensity K-distribution model versus sea-surface wind field. It is shown that the normalized intensity moments of the K-distribution measured from actual C-band SAR data well fit the theoretical ones under different wind conditions, i.e., wind regime and relative SAR-wind azimuth direction. In addition, it is observed that the K-distribution shape parameter decreases as wind speed increases. A sensitivity of the K-distribution shape parameter on incidence angle and wind direction is also observed, where the largest variability is experienced at higher incidence angles and under crosswind relative azimuth angle.

With the rapid growth of Sentinel-1 synthetic aperture radar (SAR) data, how to exploit Sentinel-1 imagery and achieve effective and robust marine surveillance are crucial problems. In this paper, we present the OpenSARShip, a dataset dedicated to Sentinel-1 ship interpretation. The OpenSARShip, providing 11 346 SAR ship chips integrated with automatic identification system messages, owes five essential properties: specificality, large scale, diversity, reliability, and public availability. These properties make sure that the OpenSARShip achieves its objectives. The first is to provide researchers a benchmark dataset to develop applicable and adaptive ship interpretation algorithms and push the performance ceilings of data analysis. The other is to provide a dataset for performing application-oriented quality assessment for Sentinel-1 imagery, which can boost their applications in a targeting way. The construction and the organization of the OpenSARShip are discussed, which show the inside of the dataset and ensure the essential properties. The elaborate geometric and scattering analyses, the benchmark for classification, and the imagery applicability assessment by using the OpenSARShip all demonstrate the applicability and potential of the dataset.