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.

Built-up area extraction from polarimetric SAR (PolSAR) imagery has a close relationship with urban planning, disaster management, etc. Since the buildings have complex geometries and may be misclassified as forests due to the significant cross-polarized scattering, built-up area extraction from PolSAR data is still a challenging problem. This paper proposes a new urban extraction method for PolSAR data. First, a multiple-component model-based decomposition method, which was previously proposed by us, is applied to detect the urban areas using the scattering powers. Second, with the sub-aperture decomposition, a new average polarimetric coherence coefficient ratio is proposed to discriminate the urban and natural areas. Finally, these two preliminary detection results are fused on the decision level to improve the overall detection accuracy. We validate our method using one dataset acquired with the Phased Array type L-band Synthetic Aperture Radar (PALSAR) system. Experimental results demonstrate that the decomposed scattering powers and the proposed polarimetric coherence coefficient ratio are both capable of distinguishing urban areas from natural areas with accuracy about 83.1% and 80.1%, respectively. The overall detection accuracy can further increase to 86.9% with the fusion of two detection results.

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.

This paper presents a new approach for the discrimination of ship responses using polarimetric SAR (PolSAR) data. The PolSAR multidimensional information is analyzed using a linear Time-Frequency (TF) decomposition approach, which permits to describe the polarimetric behavior of a ship and its background area for different azimuthal angles of observation and frequencies of illumination. This paper proposes to discriminate ships from their background by using characteristics of their polarimetric TF responses, which may be associated with the intrinsic nature of the observed natural or artificial scattering structures. A statistical descriptor related to polarimetric coherence of the signal in the TF domain is proposed for detecting ships in different complex backgrounds, including SAR azimuth ambiguities, artifacts, and small natural islands, which may induce numerous false alarms. Choices of the TF analysis direction, i.e., along separate azimuth or range axis, or simultaneously in both directions, are investigated and evaluated. TF decomposition modes including range direction perform better in terms of discriminating ships from range focusing artifacts. In comparison with original full-resolution polarimetric indicators, the proposed TF polarimetric coherence descriptor is shown to qualitatively enhance the ship/background contrast and improve discrimination capabilities. Using polarimetric RADARSAT-2 data acquired over complex scenes, experimental results demonstrate the efficiency of this approach in terms of ship location retrieval and response characterization.

The overestimation of volume scattering (OVS) is an intrinsic drawback in model-based polarimetric synthetic aperture radar (PolSAR) target decomposition. It severely impacts the accuracy measurement of scattering power and leads to scattering mechanism ambiguity. In this paper, a hierarchical extended general four-component scattering power decomposition method (G4U) is presented. The conventional G4U is first proposed by Singh et al. and it has advantages in full use of information and volume scattering characterization. However, the OVS still exists in the G4U and it causes a scattering mechanism ambiguity in some oriented urban areas. In the proposed method, matrix rotations by the orientation angle and the helix angle are applied. Afterwards, the transformed coherency matrix is applied to the four-component decomposition scheme with two refined models. Moreover, the branch condition applied in the G4U is substituted by the ratio of correlation coefficient (RCC), which is used as a criterion for hierarchically implementing the decomposition. The performance of this approach is demonstrated and evaluated with the Airborne Synthetic Aperture Radar (AIRSAR), Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR), Radarsat-2, and the Advanced Land Observing Satellite (ALOS) Phased Array type L-band Synthetic Aperture Radar (PALSAR) fully polarimetric data over different test sites. Comparison studies are carried out and demonstrated that the proposed method exhibits promising improvements in the OVS and scattering mechanism characterization.