Synthetic aperture radar (SAR) ship detection is an important part of remote sensing applications. With the development of computer vision, SAR ship detection methods based on convolutional neural network (CNN) can directly perform end-to-end detection of near-shore ship targets. However, CNN-based methods are prone to generate false targets on land areas, especially when using a rotatable bounding box (RBox) for detection. Therefore, how to reduce the false alarm rate becomes a key direction in research for SAR ship detection. In this letter, the problem of negative sample intraclass imbalance in the training stage of CNN-based detection methods is pointed out for the first time, which is considered to be an important reason for the excessive false alarm rate in the land area. Then, a method is proposed to reduce the false targets generated in the land area by CNN-based detection methods. First, an RBox-based model is proposed as the basic architecture for detection. Then, a new loss function is adopted to guide the model to balance the loss contribution of different negative samples during the training stage. The experimental results prove that the proposed method can effectively reduce the false alarm rate of the model and boost the performance of CNN-based detection methods.

Ultra-high-resolution synthetic aperture radar (SAR) has important applications in military and civilian fields. However, the acquisition of high-resolution SAR imagery poses considerable processing challenges, including limitations in traditional slant range model precision, the spatial variation in equivalent velocity, spectral aliasing, and non-negligible error introduced by stop-and-go assumption. To this end, this paper proposes an improved extended wavenumber domain imaging algorithm for ultra-high-resolution SAR to systematically address the imaging quality degradation caused by these challenges. In the proposed algorithm, the one-step motion compensation method is employed to compensate for the errors caused by orbital curvature through range-dependent envelope shift interpolation and phase function correction. Then, the interpolation based on modified Stolt mapping is performed, thereby facilitating effective separation of the range and azimuth focusing. Finally, the residual range cell migration correction is applied to eliminate range position errors, followed by azimuth compression to achieve high-precision focusing. Both simulation and spaceborne data experiments are performed to verify the effectiveness of the proposed algorithm.

Nonlinear frequency modulated (NLFM) signals can be used to enhance the resolution, anti-jamming capability, and imaging quality of synthetic aperture radar (SAR) systems through optimized design, demonstrating substantial application potential. However, in a SAR system using NLFM signals, the non-start–stop effect, caused by the continuous motion of the platform during pulse transmission and reception, introduces significant errors, resulting in target defocusing. To tackle this problem, this paper proposes a general numerical error compensation method dedicated to NLFM signals. First, the error model is correspondingly derived from the non-start–stop assumption. Then, a phase compensation method is designed through numerical calculations. Simulation experiments are performed to validate the effectiveness of the proposed method. This method provides a robust error compensation framework for high-resolution SAR systems using NLFM signals.