The polarimetric time-frequency coherent indicator has been proposed and effectively applied, especially for target detection. In this paper, based on the complex Wishart distribution, an associated asymptotic probability for the statistical test of the polarimetric time-frequency coherence is analytically derived. The goodness of fit for the probability density function is tested with Monte Carlo simulated data. It can be observed that the estimated probability density function curve fit well with the theoretical value.

Due to the complex distribution of point features in large-scene synthetic aperture radar (SAR) images, it is challenging to achieve subsequent accurate and robust registration of the images. This paper proposes a SAR point-and-line matching network (SPLM-Net) based on joint matching of sparse key points and line segments. SPLM-Net uses Graph Neural Networks (GNNs) processing to unify points, lines, and their descriptors into a wireframe structure to provide a comprehensive and local discriminant description of the image, and uses dual-softmax mechanism to determine the final feature assignment. SPLM-Net makes up for the shortcoming of high error in point feature matching of large-scale scene images. Experimental results show that the results obtained using this algorithm have significantly improved visual effects and evaluation metrics.

Polarimetric synthetic aperture radar (PolSAR) is an active microwave imaging system. Due to the coherence characteristic of PolSAR imaging, inherent coherent speckle noise exists in PolSAR images. The registration of PolSAR images is severely affected by speckle noise. Therefore, we first propose a joint filter that combines Refined-Lee filtering and polarimetric whitening filtering (PWF). The filter first applies Refined-Lee filtering to PolSAR images, which greatly reduces the speckle noise while maintaining high-resolution detailed information of the texture, and then uses PWF to normalize and whiten the polarimetric matrix to further limit the interference of speckle noise. After that, the binary robust invariant scalable keypoints (BRISK) algorithm is used to extract high-quality keypoints from the denoised PolSAR image. Then a novel Siamese Multiscale Attention Network (SMAN) is designed, which uses attention modules to construct feature descriptors with different scales. To fully utilize polarimetric information, we adopt the polarimetric covariance matrix and three polarimetric features as inputs to the network and the Second Order Similarity (SOS) as the loss function to train the network. In the keypoint matching stage, we present to use the symmetric displacement distance to further constrain the keypoint pairs obtained by the initial matching, which improves the accuracy of matching keypoint pairs. Experimental results show that our proposed method can effectively reduce the interference of speckle noise and overcome non-linear differences, geometric distortions, and differences in polarimetric scattering information to achieve accurate PolSAR image registration.

Small target detection is a very challenging problem since a small target contains only a few pixels in size. At present, many deep learning-based detection algorithms for small targets have achieved remarkable results, mainly including improvements in data augmentation, multiscale images, multiscale features, training strategies, and so on. However, these deep learning-based methods cannot select the positive and negative samples for the large-difference-scale targets well in the label assignment operation. The reason is that intersection over union (IoU), which is widely used in the most target detection networks, has great limitations for small target detection. However, in practical applications, there are often some large-difference-scale targets in synthetic aperture radar (SAR) images, especially existing some tiny targets due to the limitation of resolution. To fundamentally break the limitations of IoU, we propose to use the Bhattacharyya distance (BD) instead of the IoU metric to improve the performance of small target detection. We further revise the Bhattacharyya distance (RBD) to better measure the deviation of bounding boxes for targets with large differences in size. RBD can embed anchor-based detectors to replace the IoU metric in label assignment and nonmaximum suppression (NMS). The proposed method is evaluated on the LS-SSDD-v1.0 dataset and the experimental results show that the proposed method outperforms the state-of-the-art methods.

Motivated by the idea of low-rank prior, we propose a despeckling method based on multi-scale nonlocal low-rank model. Specially, the proposed low-rank model consists of a data fidelity term derived from the Fisher-Tippett logarithmic-space speckle distribution and a weighted nuclear norm regularization term. Furthermore, we exploit a multi-scale prior by selecting similar patches from different scales of the SAR image. The resulting optimization problem is solved by the alternating direction method of multipliers (ADMM). Experiments conducted on one real SAR image demonstrate that the proposed method can achieve comparable and even better despeckling results than state-of-the-art SAR despeckling methods, both visually and quantitatively.

This paper presents a new approach for the study of ship discrimination in complex sea ice ocean environment using single polarimetric synthetic aperture radar data and sub-spectra strategy. A statistic descriptor related to the signal coherence in the Time-Frequency domain, is proposed to enhance the ship/background contrast and improve discrimination capabilities. Using RADARSAT-2 single polarization data over complex sea ice scenes in Arctic ocean, experimental results demonstrate the efficiency of this method in terms of ship location retrieval and response characterization.

In this paper, a new azimuth ambiguities removal method is introduced for ship detection by Time-Frequency (TF) analysis. A TF coherence indicator is proposed to filter ghost echoes due to the different TF coherence characteristics between real ship target echoes and ambiguous ones. The effectiveness of this proposed TF coherence indicator for ship detection is demonstrated using single polarimetric spaceborne TerraSAR-X coherent data over the test sea/ocean site in Hongkong, China.

The presence of multiplicative noise in synthetic aperture radar (SAR) images makes segmentation and classification difficult to handle. Although a fuzzy C-means (FCM) algorithm and its variants (e.g., the FCM_S, the fast generalized FCM, the fuzzy local information C-means, etc.) can achieve satisfactory segmentation results and are robust to Gaussian noise, uniform noise, and salt and pepper noise, they are not adaptable to SAR image speckle. This letter presents a kernel FCM algorithm with pixel intensity and location information for SAR image segmentation. We incorporate a weighted fuzzy factor into the objective function, which considers the spatial and intensity distances of all neighboring pixels simultaneously. In addition, the energy measures of SAR image wavelet decomposition are used to represent the texture information, and a kernel metric is adopted to measure the feature similarity. The weighted fuzzy factor and the kernel distance measure are both robust to speckle. Experimental results on synthetic and real SAR images demonstrate that the proposed algorithm is effective for SAR image segmentation.

This paper proposes an algorithm for ship detection in complex scenes using multi-dimensional coherent Time-Frequency (TF) techniques and dual-polarization SAR data. The PolSAR multi-dimensional information is analysed by means of a linear TF decomposition approach which permits to describe the ship and background area polarimetric behaviour for different azimuth angles of observation and frequencies of illumination. A statistic descriptor related to the signal polarimetric coherence in the TF domain, is used for ship detection in different backgrounds, including the environments with presence of strong ghost ambiguities or small natural islands. Using polarimetric RADARSAT-2 data, experimental results demonstrate the efficiency of this method.

This paper addresses the statistical segmentation of SAR (Synthetic Aperture Radar) image combining PM (Perona Malik) nonlinear diffusion model and MRF (Markov Random Field) model. First, the original SAR image is filtered using the modified PM nonlinear diffusion model, in which the diffusion coefficients along the tangent direction and the normal direction are approximated and simplified. Afterwards, the filtered image is segmented using MRF model, in which the clique potential is computed using both the label configuration and the intensity information. The proposed method is marked by PM-MRF for short. Experimental results show that PM-MRF competes favorably with the traditional one to segment SAR image homogeneously.

This paper addresses the Polarimetric synthetic aperture radar (PolSAR) CFAR target detection utilizing the PolSAR synthesis technique. The optimal polarization ellipticity and orientation angles, which maximizes the ratio of the antenna receiver power between target and clutter (SCR), are searched in the co-polarized and cross-polarized channels, and the obtained antenna receiver power is named as the polarization synthesis enhancement (PSE) metric. Using the PSE metric, a data fitting based target detection scheme is presented. First, the Fisher distribution, which has extensive modeling capacity in a large set of clutters, is utilized to fit the distribution of PSE metric. Then, the corresponding “Second Kind Statistics” (SKS) parameter estimator is presented and the numerical solution of the detection threshold is also derived. Afterward, the CFAR detection is implemented. The experimental results demonstrate the enhancement results of PSE metric are almost comparable with that of the Polarimetric Matched Filter (PMF) detector. However, the computation load of PSE metric is generally less than that of the PMF. Moreover, the target detection results show the CFAR detector based on the Fisher distribution can realize the accurate target detection in homogenous and heterogeneous clutter areas.

This paper presents a method in finding tie points automatically in synthetic aperture radar (SAR) image pairs based on the extracted closed regions. There are mainly three steps during this process. The first step is to extract the closed regions in the SAR image with an image segmentation approach deducted by the geodesic active contour (GAC) model. Then, a polygonal approximation process is adopted to locate the feature points on the boundaries of these regions. With the obtained feature points, geometric hashing theory is employed to match these feature points as the tie points. A pair of simulated SAR images and a pair of high-resolution airborne SAR images are used to test and evaluate the proposed method. The experimental results show that the proposed method is effective and appropriate for the acquisitions of tie points in SAR image pairs.