Ship detection is one of important applications for synthetic aperture radar (SAR). Speckle effects usually make SAR image understanding difficult and speckle reduction becomes a necessary pre-processing step for majority SAR applications. This work examines different speckle reduction methods on SAR ship detection performances. It is found out that the influences of different speckle filters are significant which can be positive or negative. However, how to select a suitable combination of speckle filters and ship detectors is lack of theoretical basis and is also data-orientated. To overcome this limitation, a speckle-free SAR ship detection approach is proposed. A similar pixel number (SPN) indicator which can effectively identify salient target is derived, during the similar pixel selection procedure with the context covariance matrix (CCM) similarity test. The underlying principle lies in that ship and sea clutter candidates show different properties of homogeneity within a moving window and the SPN indicator can clearly reflect their differences. The sensitivity and efficiency of the SPN indicator is examined and demonstrated. Then, a speckle-free SAR ship detection approach is established based on the SPN indicator. The detection flowchart is also given. Experimental and comparison studies are carried out with three kinds of spaceborne SAR datasets in terms of different polarizations. The proposed method achieves the best SAR ship detection performances with the highest figures of merits (FoM) of 97.14%, 90.32% and 93.75% for the used Radarsat-2, GaoFen-3 and Sentinel-1 datasets, accordingly.

Ship monitoring is an important application of synthetic aperture radar (SAR). The constant false alarm rate (CFAR) methods are commonly used for ship detection. However, CFAR detectors usually face challenges for inshore dense ship detection. Due to the significant mixture of ship candidates and sea clutters within the clutter window, the detection threshold may be overestimated leading to many missed detections. To mitigate this issue, a superpixel-level CFAR detector is proposed. The main contribution contains two aspects. First, a labeling procedure is established for pure clutter superpixels and mixture superpixels discrimination in terms of unsupervised clustering. Second, a nonlocal topology strategy is proposed to adaptively determine a sufficient number of pure clutter superpixels for detection threshold estimation. In this vein, an adaptive superpixel-level CFAR approach is constructed and validated with Radarsat-2, Sentinel-1, and AIRSARShip-1 data sets. Comparison studies demonstrate the superiority of the proposed method. Compared with a traditional CFAR detector and two recent superpixel methods, the proposed method achieves clearly better performance for inshore dense ship regions.

Polarimetric radar, which can acquire full polarization information, has become mainstream in microwave remote sensing. However, radar target scattering responses are strongly orientation dependent, which makes applications such as target detection and recognition more difficult. Polarimetric roll-invariant features, which are independent of target orientations along the radar line of sight (LOS), exhibit great importance and have achieved successful applications in many fields. During the development of radar polarimetry, a number of polarimetric roll-invariant features have been found and reported. A comprehensive summary and investigation of these polarimetric roll-invariant features is necessary and valuable for current studies and further developments. This article is dedicated to this purpose and contains two main ­contributions. First, according to the authors’ knowledge, polarimetric roll-invariant features are thoroughly investigated. Based on their original derivation fashion, they are partitioned into six groups, including those derived from the Sinclair matrix, the Graves matrix, the polarimetric coherency and covariance matrices, eigenvalue–eigenvector-based decompositions, polarimetric coherence/­correlation patterns, and the Kennaugh matrix. Meanwhile, their expressions, inner relationships, and physical interpretations are further examined and presented. Second, since ship detection is an important application for polarimetric synthetic aperture radar (PolSAR), the potential of these independent features for PolSAR ship detection is investigated and demonstrated. Experimental studies with Radar Satellite-2 ( RADARSAT-2 ) and Gaofen-3 PolSAR datasets are carried out. It is validated that the selected polarimetric roll-invariant features exhibit superior performance compared with normal ­polarimetric features.

Ship detection in polarimetric SAR (PolSAR) image plays an important role in marine monitoring. From the viewpoint of the attention mechanism, ship targets can be salient candidates from sea clutter. A novel saliency detector for PolSAR ship detection has been proposed in this paper. The core idea is to explore the different scattering mechanisms between ships and sea clutter in low and medium sea conditions. The scattering mechanism differences are measured by the similarity test of polarimetric covariance matrices. A new saliency feature named similar pixel number (SPN) is proposed by recording the similar pixels within a moving window and a saliency detection method is developed thereafter. The proposed ship detection method mainly contains three steps. Firstly, the similarity test is applied between the central pixel and its neighborhood. Then, SPN feature is generated based on the result of similarity test by counting the number of similar pixels. Finally, with thresholding and morphological filtering procedures, ship targets can be extracted from the saliency feature map. Experimental studies with Radarsat-2 and GaoFen-3 data validate the advantages of the proposed method. Quantitative comparisons illustrate that the proposed method achieves the best detection performance with detection quality factors higher than 97% for both datasets over inshore dense and offshore sparse areas.

For polarimetric synthetic aperture radar (PolSAR) data, the correlation of two polarization channels is sensitive to the target orientation relative to the sensor's illumination direction. In this letter, the concept of polarimetric correlation pattern is proposed to explore this scattering diversity. The core idea is to extend the polarimetric correlation from a fixed angle to the rotation domain along the radar line of sight. A set of new polarimetric features are derived, and three features with high target-to-clutter ratio (TCR) are selected for PolSAR ship detection. The proposed ship detection method mainly contains three steps: the features selection, thresholding procedure, and morphological filtering. Experimental studies with Radarsat-2 and GaoFen-3 data validate the advantage of the proposed approach, especially for inshore dense ship discrimination.

Man-made target recognition is of great significance for many applications within microwave remote sensing. The scattering diversity of various man-made target structures makes radar target identification a difficult task. This work aims at mitigating this issue by mining and utilization of man-made target scattering diversity in polarimetric rotation domain with the interpretation tool of polarimetric correlation pattern. The optimal polarimetric roll-invariant feature set is collected from polarimetric correlation pattern. Then, a polarimetric roll-invariant feature coding scheme is developed for man-made target structure recognition. Moreover, polarimetric radar measurement errors in terms of channel coupling and imbalance are also considered. Experimental studies with electromagnetic computation datasets including canonical structures and an unmanned aerial vehicle (UAV) target and real spaceborne polarimetric synthetic aperture radar (PolSAR) data of a ship target are carried out. Compared with the Cameron decomposition, the proposed method exhibits better recognition performance and stronger robustness, especially for oriented man-made structures.