With the development of satellite technology, up to date imaging mode of synthetic aperture radar (SAR) satellite can provide higher resolution SAR imageries, which benefits ship detection and instance segmentation. Meanwhile, object detectors based on convolutional neural network (CNN) show high performance on SAR ship detection even without land-ocean segmentation; but with respective shortcomings, such as the relatively small size of SAR images for ship detection, limited SAR training samples, and inappropriate annotations, in existing SAR ship datasets, related research is hampered. To promote the development of CNN based ship detection and instance segmentation, we have constructed a High-Resolution SAR Images Dataset (HRSID). In addition to object detection, instance segmentation can also be implemented on HRSID. As for dataset construction, under the overlapped ratio of 25%, 136 panoramic SAR imageries with ranging resolution from 1m to 5m are cropped to 800 × 800 pixels SAR images. To reduce wrong annotation and missing annotation, optical remote sensing imageries are applied to reduce the interferes from harbor constructions. There are 5604 cropped SAR images and 16951 ships in HRSID, and we have divided HRSID into a training set (65% SAR images) and test set (35% SAR images) with the format of Microsoft Common Objects in Context (MS COCO). 8 state-of-the-art detectors are experimented on HRSID to build the baseline; MS COCO evaluation metrics are applicated for comprehensive evaluation. Experimental results reveal that ship detection and instance segmentation can be well implemented on HRSID.

As the electromagnetic environment in battlefields is more and more complex, automatic modulation recognition for radar signals is becoming vital and challenging. Traditional methods are more likely to cause lower recognition accuracy with higher computational complexity in low signal-to-noise ratio (SNR). Feature redundancy especially for handcrafted features is one of the shortcomings of deep-learning-based methods. In this article, a novel end-to-end sequence-based network that consists of a shallow convolutional neural network, a bidirectional long short-term memory (Bi-LSTM) network strengthening with a self-attention mechanism, and a dense neural network is constructed to recognize eight kinds of intrapulse modulations of radar signals. The autocorrelation functions of received radar signals are first calculated as autocorrelation features. Then, these features are employed as inputs of the proposed network which owns significant sequence processing advantages and adaptive selection ability of features. Finally, the proposed network outputs prediction modulations directly. The simulation results verify the robustness and effectiveness of autocorrelation features. And the proposed network achieves about 61.25% accuracy at −20 dB and more than 95% accuracy at −10 dB. Compared with four state-of-the-art networks, the proposed network has better recognition performance especially at low SNRs with much lower computational complexity. Results on measured signals also demonstrate that the proposed network outperforms these four networks.

Compressed sensing (CS) demonstrates significant potential to improve image quality in 3-D millimeter-wave imaging compared with conventional matched filtering (MF). However, existing sparsity-driven 3-D imaging algorithms always suffer from large-scale storage, excessive computational cost, and nontrivial tuning of parameters due to the huge-dimensional matrix–vector multiplication in complicated iterative optimization steps. In this article, we present a novel range migration (RM) kernel-based iterative-shrinkage thresholding network, dubbed as RMIST-Net, by combining the traditional model-based CS method and data-driven deep learning method for near-field 3-D millimeter-wave (mmW) sparse imaging. First, the measurement matrices in ISTA optimization steps are replaced by RM kernels, by which matrix–vector multiplication is converted to the Hadamard product. Then, the modified ISTA optimization is unrolled into a deep hierarchical architecture, in which all parameters are learned automatically instead of manually tuned. Subsequently, 1000 pairs of oracle images with randomly distributed targets and their corresponding echoes are simulated to train the network. A well-trained RMIST-Net produces high-quality 3-D images from range-focused echoes. Finally, we experimentally prove that RMIST-Net is capable process $512 \times 512$ large-scale imaging tasks within 1 s. Besides, we compare RMIST-Net with other state-of-the-art methods in near-field 3-D imaging applications. Both simulations and real-measured experiments demonstrate that RMIST-Net produces impressive reconstruction performance while maintaining high computational speed compared with conventional and sparse imaging algorithms.

Since the Compressed Sensing (CS) theory broke through the limitation of the traditional Nyquist sampling theory, it has attracted extensive attention in the field of microwave imaging. However, in three dimensional (3-D) microwave sparse reconstruction application, conventional CS-based algorithms always suffer from huge computational cost. In this paper, a novel 3-D microwave sparse reconstruction method based on a complex-valued sparse reconstruction network (CSR-Net) is proposed, which converts complex number operations into matrix operations for real and imaginary parts. Using the “unfolding + network approximate” scheme, each iteration process of CS-based iterative threshold optimization is designed as a block of CSR-Net, and a modified shrinkage term is introduced to improve the convergence performance of the approach. In addition, CSR-Net adopts a convolutional neural network module to replace a nonlinear sparse representation process, which dramatically reduces computational complexity and improves reconstruction performance over conventional CS-based iterative threshold optimization algorithms. Then, we divide the 3-D scene into a series of 2-D slices, and a phase correction scheme is adopted to ensure that the whole 3-D scene can be reconstructed with measurement matrix of a slice. Moreover, an efficient Position-Amplitude-Random (PAR) training method without additional real-measured data is employed for the proposed network, which effectively train the CSR-Net without enough real-measured data. Extensive experiment results demonstrate that CSR-Net outperforms both conventional iterative threshold optimization methods and deep network-based ISTA-NET-plus large margins. Its speed and reconstruction accuracy in 3-D imaging can achieve a state-of-the-art level.

Bounding boxes have been widely implemented into aerial object detection for their simplicity. They perform instance-level locations with the coordinates and orientation for each target. However, the defects such as coarse edge information impede semantic interpretation in Earth observation. Besides, in terms of the aerial imaging instruments, it is essential to recognize the exterior appearance and contour of the objects. In this article, we propose a novel aerial instance segmentation method termed the adaptive region of interest (RoI) extraction network (ARE-Net), which bridges the gap of accurately delineating instances under the complex background of aerial images. To exert instance segmentation under the proprietary property, e.g., complex background and densely distributed instances, of aerial images, RoIs are pooled from multilevel feature maps and integral region proposals. On this basis, global attention RoI extractor (GA-RoIE) and perceptual RoI extractor (PRoIE) are, respectively, introduced for detection branch and mask branch to perform adaptive RoI extraction for aerial images. Meanwhile, to reconcile the probability distribution regional distribution of pixelwise prediction in aerial images, we present the adaptive compound loss function to improve the integrating degree of the predicted binary mask to the ground-truth mask. In addition, we adopt RegNetx with deformable convolution to optimize ARE-Net and name it as R-ARE-Net. Despite implementing pixelwise prediction, comprehensive experiments on iSAID and NWPU VHR-10 instance segmentation datasets still have verified the effectiveness and efficiency of ARE-Net and R-ARE-Net. Experimental results indicate that our proposed methods receive the highest AP value (38.0% AP on iSAID and 64.2% AP on NWPU VHR-10 instance segmentation dataset) and the lowest floating-point operations (FLOPs) and parameters’ consumption (~46% reduced FLOPs and 61.5% reduced parameters than SCNet) among the mainstream methods. Besides, the false alarms, missing segmentations, poorly predicted masks, and undersegmentations that appeared in the mainstream methods can be avoided to some extend for R-ARE-Net.

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 .