Traditional constant false alarm rate (CFAR) based ship target detection methods do not work well in complex conditions, such as multi-scale situations or inshore ship detection. With the development of deep learning techniques, methods based on convolutional neural networks (CNN) have been applied to solve such issues and have demonstrated good performance. However, compared with optical datasets, the number of samples in SAR datasets is much smaller, thus limiting the detection performance. Moreover, most state-of-the-art CNN-based ship target detectors that focus on the detection performance ignore the computation complexity. To solve these issues, this paper proposes a lightweight densely connected sparsely activated detector (DSDet) for ship target detection. First, a style embedded ship sample data augmentation network (SEA) is constructed to augment the dataset. Then, a lightweight backbone utilizing a densely connected sparsely activated network (DSNet) is constructed, which achieves a balance between the performance and the computation complexity. Furthermore, based on the proposed backbone, a low-cost one-stage anchor-free detector is presented. Extensive experiments demonstrate that the proposed data augmentation approach can create hard SAR samples artificially. Moreover, utilizing the proposed data augmentation approach is shown to effectively improves the detection accuracy. Furthermore, the conducted experiments show that the proposed detector outperforms the state-of-the-art methods with the least parameters (0.7 M) and lowest computation complexity (3.7 GFLOPs).

The squinted imaging mode has been successfully applied to synthetic aperture radar (SAR) mounted on moving platforms of high speed. The developed focusing algorithms for squint SAR data usually perform the linear range walk correction (LRWC) in the azimuth time domain as the first step, to mitigate the bulk range-azimuth coupling. Unfortunately, when applied on high-speed platforms with large squint angle and large azimuth scene length, these algorithms for airborne SAR considering only the first-order azimuth dependence of the Doppler frequency-modulation (FM) rate will introduce a large residual phase error, and therefore degrade the final radar image quality. Meanwhile, those algorithms, originally designed for full-aperture data, cannot be applied directly to the small-aperture data. Due to these problems, a high-order phase correction approach (HPCA) combined with SPECAN operation for focusing high-squint SAR (HS-SAR) small-aperture data is developed in this paper to deal with the above issues. In this approach, both the first- and the second-order azimuth dependence of the Doppler FM rate are compensated for, and the same to the cubic phase term, by introducing a correction phase term in the azimuth frequency domain. The presented approach has been successfully used to focus the real airborne radar data recently.

Motion errors are inevitably introduced when data is acquired and considerably degrade the image quality in terms of geometric resolution, radiometric accuracy and image contrast, especially in high‐resolution spotlight synthetic aperture radar (SAR) imagery. In this study, the authors present a novel two‐dimensional (2D) autofocus algorithm directly inserted into polar format algorithm, which compensates the envelop error and the phase error sequentially. A coarse error correction is first performed by global positioning system or inertial navigation system in the range‐compressed domain, then a new envelop compensation strategy, stage‐by‐stage approach, is designed, obtaining promising results for removing range cell migration after 2D interpolation. Additionally, a weighed contrast enhancement autofocus algorithm based on spatially variant model is developed to compensate for the residual phase error, which remarkably improves the estimation accuracy. The presented algorithm is very robust to deal with substantial errors over a variety of scenes even in conditions of homogenous areas with no prominent point scatterers and enables the utilisation of fast Fourier transform. The experimental results obtained by the proposed algorithm confirm that the analysis extends well to realistic situations.

Hyperspectral imaging (HSI) has been widely investigated within the context of computational imaging due to the high dimensional challenges for direct imaging. However, existing computational HSI approaches are mostly designed for the visible to near-infrared waveband, whereas less attention has been paid to the mid-infrared spectral range. In this paper, we report a novel mid-infrared compressive HSI system to extend the application domain of mid-infrared digital micromirror device (MIR-DMD). In our system, a modified MIR-DMD is combined with an off-the-shelf infrared spectroradiometer to capture the spatial modulated and compressed measurements at different spectral channels. Following this, a dual-stage image reconstruction method is developed to recover infrared hyperspectral images from these measurements. In addition, a measurement without any coding is used as the side information to aid the reconstruction to enhance the reconstruction quality of the infrared hyperspectral images. A proof-of-concept setup is built to capture the mid-infrared hyperspectral data of 64 pixels × 48 pixels × 100 spectral channels ranging from 3 to 5 μm, with the acquisition time within one minute. To the best of our knowledge, this is the first mid-infrared compressive hyperspectral imaging approach that could offer a less expensive alternative to conventional mid-infrared hyperspectral imaging systems.

Accurate range modeling, cross-range-dependent range migration, and space-variant Doppler parameter are main issues to be solved in processing high-squint synthetic aperture radar (SAR) data acquired from maneuvering platforms. A frequency domain backprojection algorithm, based on local Cartesian coordinate (LCC) and subregion range cell migration correction, is proposed to deal with these problems. With the proposed algorithm, the range model is built in an LCC system to accurately match the signal characteristics after range walk correction. Then, the compensation of cross-range-dependent range migration is implemented based on properly divided subregions after azimuth spectrum filtering. Finally, the space-variant Doppler parameter and higher order phase terms are coherently integrated in range-Doppler domain to get the focused subregion images with full resolution of the synthetic aperture. The final image of the entire scene is obtained by directly connecting all subregion images. The results of simulated and real SAR data validate the proposed algorithm.

Compared with frequency-domain algorithms, time-domain algorithms (TDAs) can achieve image focusing under the conditions of arbitrary trajectories and large integration angles. However, the interpolations in both range and bearing angle directions are required in the coordinate transformation of fast TDAs, which inevitably increases the computational load and introduces interpolation errors. In this paper, a fast time-domain imaging and corresponding autofocus method based on the hybrid coordinate (HC) system is proposed. First, the interpolation operation is optimized in the process of fast TDAs. It transforms the 2-D interpolation into 1-D interpolation only in the bearing angle direction, which improves the execution efficiency and reduces the interpolation error. Next, a 3-D trajectory deviation estimation method based on Gauss-Newton iteration is investigated for the motion compensation in the HC system. By iterative optimization, the 3-D motion errors during the flight are estimated accurately, and the space-variant phase error is compensated precisely. This method has good robustness and universality. Simulation results and real data processing demonstrate the effectiveness and the practicability of the method presented in this paper.