Zhou Li

Synthetic aperture radar (SAR) image intelligence interpretation is always a challenging task due to its special imaging mechanism. This paper focuses on the aircraft detection and recognition in large-scale SAR images. The objects in large-size SAR images are usually sparsely distributed and tend to be highly concentrated in certain areas. Therefore, the detection and classification in SAR images are affected easily by imaging environment, leading to ineffective and instability. It suffers from time-consuming, high false-alarm rate, and low recognition rate. To alleviate these problems, a joint probability detector (JPD) integrated with scattering attention (SA) is designed for aircraft detection and classification. Based on the fact that all aircraft are parked in the airport area, an airport detection method with mixed strategy (ADMS) is proposed to obtain the valid region (airport), narrowing downing the scope of detection area, increasing the detection efficiency as well as suppressing a lot of false alarms outside the airport. Considering that it's easy to generate false alarms within the airport on account of clutter scattering interference, a two-stage detector is introduced to improve the accuracy by replacing a stronger region proposal network (RPN) and optimizing a lower bound to a joint probabilistic objective over two stages. Furthermore, the classification encoder module incorporates scattering attention into convolution neural network to leverage the distribution relation among strong scattered points to enhance the classification performance. Finally, extensive experiments on the ZKXT and GaoFen-3 datasets verify the effectiveness and superiority of the proposed approach, and surpass runner up by 5.3% with mF1 indicator. On a large GaoFen3 image (16550× 11945 pixels), the proposed method improves the detection speed by 187.52%, without sacrificing the accuracy. More importantly, the proposed method has been applied to the SAR image interpretation and achieved the expected effect in the actual application.

The development of deep learning has significantly boosted the development of ship detection in synthetic aperture radar (SAR) images. Most previous works rely on the convolutional neural networks (CNNs), which extract characteristics through local receptive fields and are sensitive to noise. Moreover, these detectors have limited performance in large-scale and complex scenes due to the strong interference of inshore background and the variability of target imaging characteristics. In this article, a novel SAR ship detection framework is proposed, which establishes the pyramid vision transformer (PVT) paradigm for multiscale feature representations in SAR images and, hence, is referred to as PVT-SAR. It breaks the limitation of the CNN receptive field and captures the global dependence through the self-attention mechanism. Since the difficulties of object detection in SAR and natural images are quite different, directly applying the existing transformer structure, such as PVT-small, cannot achieve satisfactory performance for SAR object detection. Compared with the PVT, overlapping patch embedding and mixed transformer encoder modules are incorporated to overcome the problems of densely arranged targets and insufficient data. Then, a multiscale feature fusion module is designed to further improve the detection ability for small targets. Moreover, a normalized Gaussian Wasserstein distance loss is employed to suppress the influence of scattering interference at the ship's boundary. The superiority of the proposed PVT-SAR detector over several state-of-the-art-oriented bounding box detectors has been evaluated in both inshore and offshore scenes on two commonly used SAR ship datasets (i.e., RSSDD and HRSID).

SPIE Digital Library Proceedings

Sidelobe reduction is a very primary task for synthetic aperture radar (SAR) images. Various methods have been proposed for broadside SAR, which can suppress the sidelobes effectively while maintaining high image resolution at the same time. Alternatively, squint SAR, especially highly squint SAR, has emerged as an important tool that provides more mobility and flexibility and has become a focus of recent research studies. One of the research challenges for squint SAR is how to resolve the severe range-azimuth coupling of echo signals. Unlike broadside SAR images, the range and azimuth sidelobes of the squint SAR images no longer locate on the principal axes with high probability. Thus the spatially variant apodization (SVA) filters could hardly get all the sidelobe information, and hence the sidelobe reduction process is not optimal. In this paper, we present an improved algorithm called double spatially variant apodization (D-SVA) for better sidelobe suppression. Satisfactory sidelobe reduction results are achieved with the proposed algorithm by comparing the squint SAR images to the broadside SAR images. Simulation results also demonstrate the reliability and efficiency of the proposed method.