Ship instance segmentation of high-resolution (HR) synthetic aperture radar (SAR) images is a valuable and challenging task due to the complex scattering and noise properties. In this article, we pioneered the construction of the low-level feature to discriminate the ships and complemented the super-resolution (SR) denoising techniques in the network modules, termed low-level feature guided network (LFG-Net), for precise ship instance segmentation in SAR images. LFG-Net consists of the low-level feature concerned pyramid (LFCP), the high-resolution feature interaction module (HR-FIM), and the compression recovery segmentation branch (CRSB). LFCP extends the vanilla feature pyramid network (FPN) with the ${P_{1}}$ layer and complements SR techniques to capture the regional and texture information at the image level for small object segmentation. HR-FIM interacts with the bounding box region of interest (RoI) feature and mask RoI feature at the instance level with HR techniques to enhance the mask RoI feature. CRSB aims at recovering the HR mask predictions to improve the ship segmentation performance. Comprehensive experiments on high-resolution SAR images dataset (HRSID), polygon segmentation SAR ship detection dataset (PSeg-SSDD), and AirSARShip indicate that LFG-Net* achieves 11.7%, 6.3%, and 12.7% AP increments compared with the Mask R-CNN baseline, respectively. Besides, it receives 9.5%, 4.9%, and 7.3% AP increments compared with the state-of-the-art method, which bridges the gap of instance segmentation precision in SAR images. In terms of the visualized instance segmentation results, LFG-Net ^* is capable of segmenting the complex scenes, e.g., the adjacent distributed ships and ships with strong reflection noise interference, in SAR images. The code is available at: https://github.com/Evarray/LFG-Net .

Instance segmentation in remote sensing images is challenging due to the object-level discrimination and pixel-level segmentation for the objects. In remote sensing applications, instance segmentation adopts the instance-aware mask, rather than horizontal bounding box and oriented bounding box in object detection, or category-aware mask in semantic segmentation, to interpret the objects with the boundaries. Despite these distinct advantages, versatile instance segmentation methods are still to be discovered for remote sensing images. In this paper, an efficient instance segmentation paradigm (EISP) for interpreting the synthetic aperture radar (SAR) and optical images is proposed. EISP mainly consists of the Swin Transformer to construct the hierarchical features of SAR and optical images, the context information flow (CIF) for interweaving the semantic features from the bounding box branch to mask branch, and the confluent loss function for refining the predicted masks. Experimental conclusions can be drawn on the PSeg-SSDD (Polygon Segmentation—SAR Ship Detection Dataset) and NWPU VHR-10 instance segmentation dataset (optical dataset): (1) Swin-L, CIF, and confluent loss function in EISP acts on the whole instance segmentation utility; (2) EISP* exceeds vanilla mask R-CNN 4.2% AP value on PSeg-SSDD and 11.2% AP on NWPU VHR-10 instance segmentation dataset; (3) The poorly segmented masks, false alarms, missing segmentations, and aliasing masks can be avoided to a great extent for EISP* in segmenting the SAR and optical images; (4) EISP* achieves the highest instance segmentation AP value compared to the state-of-the-art instance segmentation methods.

The sparse imaging method based on compressed sensing (CS) is widely used in the field of millimeter-wave (MMW) synthetic aperture radar (SAR) imaging. However, 3-D sparse imaging is limited by the difficult parameter tuning, the huge computational load, and the low processing efficiency. In addition, due to the motion errors and model mismatch, it is difficult to obtain well-focused results without error correction techniques. To address these issues, we propose a deep learning framework that integrates 3-D sparse imaging and autofocusing, named 3-D sparse autofocusing network (SAF-3DNet) for MMW SAR data processing. The network is constructed based on an auto-encoder, which can optimize parameters without effective ground truth. The backbone structure of the encoder is expanded by approximate message-passing (AMP), and the operators in the frequency domain are used to replace the traditional matrix-vector CS model, which avoids large-scale matrix multiplication and other operations, and greatly improves the operation efficiency. In addition, the 2-D phase error estimation in the cross-range plane is embedded into the sparse imaging models, enabling simultaneous 3-D imaging and autofocusing. The decoder is designed as a mapping from the autofocusing results to the echo data. Experimental results based on both simulated and measured data demonstrate the proposed SAF-3DNet can achieve well-focused 3-D reconstruction within an ephemeral time, which expresses the potential of 3-D MMW SAR real-time and high-quality imaging.

The application of the compressed sensing (CS) method in the radar field enables the radar imaging system to satisfy both low data cost and high reconstruction quality; however, it is accompanied by enormous iterative operations and difficult adjustments of parameters. In this article, we propose a learning-based split unfolding framework, dubbed as split iterative sparse reconstruction network (SISR-Net), for near-field 3-D millimeter-wave (mmW) radar sparse imaging. First, a split iterative sparse reconstruction algorithm, i.e., SISRA, is proposed to theoretically guide the structure of the imaging framework. Subsequently, by combining the model-based CS method and data-driven deep learning method, SISR-Net is constructed by SISRA to produce 3-D mmW radar images efficiently with excellent explainability and generalization ability. Combining the radar-imaging kernel, echo-generation kernel, and the split Bregman method, the efficiency and stability of SISR-Net are guaranteed, and all the parameters are layer-varied and learned steadily by end-to-end training to improve the convergence and robustness of the imaging network. Simulated data and echo from a high-resolution mmW radar dataset 3DRIED are used to train and test the SISR-Net based on the Adam optimizer. For both simulation and extensive 3-D mmW radar-measured experiments, the proposed SISR-Net outperforms other state-of-the-art imaging methods in terms of imaging accuracy and generalization ability.

Spaceborne synthetic aperture radar (SAR) is a promising remote sensing technique, as it can produce high-resolution imagery over a wide area of surveillance with all-weather and all-day capabilities. However, the spaceborne SAR sensor may suffer from severe radio frequency interference (RFI) from some similar frequency band signals, resulting in image quality degradation, blind spot, and target loss. To remove these RFI features presented on spaceborne SAR images, we propose a multi-dimensional calibration and suppression network (MCSNet) to exploit the features learning of spaceborne SAR images and RFI. In the scheme, a joint model consisting of the spaceborne SAR image and RFI is established based on the relationship between SAR echo and the scattering matrix. Then, to suppress the RFI presented in images, the main structure of MCSNet is constructed by a multi-dimensional and multi-channel strategy, wherein the feature calibration module (FCM) is designed for global depth feature extraction. In addition, MCSNet performs planned mapping on the feature maps repeatedly under the supervision of the SAR interference image, compensating for the discrepancies caused during the RFI suppression. Finally, a detailed restoration module based on the residual network is conceived to maintain the scattering characteristics of the underlying scene in interfered SAR images. The simulation data and Sentinel-1 data experiments, including different landscapes and different forms of RFI, validate the effectiveness of the proposed method. Both the results demonstrate that MCSNet outperforms the state-of-the-art methods and can greatly suppress the RFI in spaceborne SAR.