Ship detection is a crucial application of synthetic aperture radar (SAR). Most recent studies have relied on convolutional neural networks (CNNs). CNNs tend to struggle in gathering adequate contextual information through local receptive fields and are also susceptible to noise. Inshore scenes in SAR images are plagued by substantial background noise, so achieving high-accuracy ship detection of arbitrary orientations within complex scenes remains an ongoing challenge when relying solely on CNNs. To address the above challenges, this paper presents an anchor-free method based on transformers and adaptive features, namely, SAD-Det, which can detect rotationally invariant ship targets with high average precision in SAR images. Specifically, a transformer-based backbone network called the ship spatial pooling pyramid vision transformer (SSP-PVT) is proposed to enhance the long-range dependencies and obtain sufficient contextual information for ships in SAR images. In addition, a neck network called the adaptive feature pyramid network (AFPN) is designed to enhance the ability of ship feature adaptation by adding fusion factors to feature layers in SAR images. Finally, a head network called the deformable head (DeHead) is constructed to make the network more adaptable to the characteristics of ships by adaptively detecting the spatial sampling positions of the targets in SAR images. The effectiveness of the proposed method is verified by experiments on two publicly available datasets, i.e., SAR ship detection dataset (SSDD) and rotated ship detection dataset in SAR images (RSDD-SAR). Compared with other arbitrarily oriented object detection methods, the proposed method achieves state-of-the-art detection performance.

Lightweight ship detection is an important application of synthetic aperture radar (SAR). The prevailing trend in recent research involves employing a detection framework based on convolutional neural networks (CNNs) and horizontal bounding boxes (HBBs). However, CNNs with local receptive fields fall short in acquiring adequate contextual information and exhibit sensitivity to noise. Moreover, HBBs introduce significant interference from both the background and adjacent ships. To overcome these limitations, this paper proposes a lightweight transformer-based method for detecting arbitrarily oriented ships in SAR images, called LD-Det, which excels at promptly and accurately identifying rotating ship targets. First, light pyramid vision transformer (LightPVT) is introduced as a lightweight backbone network. Built upon PVT v2-B0-Li, it effectively captures the long-range dependencies of ships in SAR images. Subsequently, multi-scale deformable feature pyramid network (MDFPN) is constructed as a neck network, utilizing the multi-scale deformable convolution (MDC) module to adjust receptive field regions and extract ship features from SAR images more effectively. Lastly, shared deformable head (SDHead) is proposed as a head network, enhancing ship feature extraction with the combination of deformable convolution operations and a shared parameter structure design. Experimental evaluations on two publicly available datasets validate the efficacy of the proposed method. Notably, the proposed method achieves state-of-the-art detection performance when compared with other lightweight methods in detecting rotated targets.

Airborne Synthetic Aperture Radar (SAR) Tomography (TomoSAR) technology facilitates the extraction of three-dimensional (3D) information of target scatterers. However, phase screens induced by radar platform trajectory errors causes TomoSAR defocusing, which adversely affects the accuracy of the forest vertical structure retrieval. Additionally, the phase screens exhibit space-variant characteristics, which significantly degrade the calibration performance estimated by traditional phase gradient autofocus (PGA). This paper proposes an improved phase calibration method synthesizing unconstrained optimization model and weighted PGA (WPGA), which effectively address the above challenges. Firstly, the SAR data stack is segmented into multiple subareas by assuming that phase screens are space-invariant within each small area, which reduces complexity and improves computational efficiency. Secondly, a WPGA method synthesizing the scatterer heights derived from an unconstrained optimization model is proposed to estimate the phase screens. Simulation experiments are conducted to validate the effectiveness of the proposed phase calibration method. Furthermore, the full-polarization SAR data stacks acquired by the BorTomoSAR campaign are used for tomographic focusing analysis. Experimental results demonstrate that the proposed method accurately estimates the phase screens at the P, L, and S bands, providing a efficient solution for the forest vertical structure retrieval.

The Sentinel-1 (S1) satellites, operating at C-band in Terrain Observation by Progressive Scans (TOPS) mode, provide unprecedented opportunities for measuring large-scale tectonic motions using Interferometric Synthetic Aperture Radar (InSAR). However, the azimuth-varying Doppler centroid imposes stringent coregistration requirements, for which the Enhanced Spectral Diversity (ESD) method is typically employed to achieve the required high-precision azimuth coregistration. In practical applications, however, the ESD method is inevitably affected by various disturbances, especially by pronounced large-scale and non-stationary azimuth deformation. Such disturbances not only degrade the coregistration accuracy of the ESD method but also cause phase jumps at burst edges, thereby complicating the subsequent phase unwrapping. To address these issues, this study proposes an improved coregistration strategy that integrates the Local Iterative Pixel Offset Tracking (LI-POT) and Weighted ESD (WESD) to mitigate the effects of azimuth deformation and enhance the robustness of the ESD method. Furthermore, the Coherence Gradient Consistency (CGC) indicator is introduced to embed the proposed method into the standard processing workflow, enabling automated selection of appropriate coregistration strategies. The effectiveness of the proposed method was validated using S1 TOPS repeat-pass data acquired over four representative regions. By applying the proposed method, the phase jumps observed in the conventional processing were effectively corrected, demonstrating its superior accuracy and stability in complex deformation scenarios.

In airborne synthetic aperture radar (SAR) imaging, slant-range errors vary across the swath, making phase errors range-dependent. However, the conventional phase gradient autofocus (PGA) method assumes a range-invariant phase model and becomes unreliable when range-dependent phase errors are pronounced. Although range-partitioned PGA can substantially improve focusing performance, it may still introduce block-dependent azimuth shifts after compensation, causing geometric distortion in the focused image. To address this problem, this paper proposes a lightweight post-autofocus distortion-correction method for SAR images processed by range-partitioned PGA. Instead of re-estimating the full residual phase, the method operates on the block-wise phase-error estimates after global linear-phase removal, extracts the distortion-related linear trend using a sliding-window fitting strategy, converts it into azimuth-shift profiles, and performs sinc-based realignment. The proposed method is validated using both simulation and real unmanned aerial vehicle (UAV) SAR data. Experimental results demonstrate that the method effectively corrects geometric distortion while preserving the focusing gain achieved by range-partitioned PGA. In two representative real-data regions, the azimuth misalignment is reduced from 20 pixels to 3 pixels and from 34 pixels to 2 pixels, respectively.