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Synthetic aperture radar (SAR) ship detection and classification has gained unprecedented attention due to its important role in maritime transportation. Many deep learning-based detectors and classifiers have been successfully applied and achieved great progress. However, ships in SAR images present discrete and multi-centric features, and their scattering characteristics and edge information are sensitive to variations in target attitude angles (TAAs). These factors pose challenges for existing methods to obtain satisfactory results. To address these challenges, a novel target attitude angle-guided network (TAG-Net) is proposed in this article. The core idea of TAG-Net is to leverage TAA information as guidance and use an adaptive feature-level fusion strategy to dynamically learn more representative features that can handle the target imaging diversity caused by TAA. This is achieved through a TAA-aware feature modulation (TAFM) module. It uses the TAA information and foreground information as prior knowledge and establishes the relationship between the ship scattering characteristics and TAA information. This enables a reduction in the intra-class variability and highlights ship targets. Additionally, considering the different requirements of the detection and classification tasks for the scattering information, we propose a layer-wise attention-based task decoupling detection head (LATD). Unlike general deep learning methods that use shared features for both detection and classification tasks, LATD extracts multi-level features and uses layer attention to achieve feature decoupling and select the most suitable features for each task. Finally, we introduce a novel salient-enhanced feature balance module (SFB) to provide richer semantic information and capture the global context to highlight ships in complex scenes, effectively reducing the impact of background noise. A large-scale ship detection dataset (LSSDD+) is used to verify the effectiveness of TAG-Net, and our method achieves state-of-the-art performance. [ABSTRACT FROM AUTHOR]

Range sidelobe is very common in synthetic aperture radar (SAR) images, particularly when imaging scene includes strongly scattering targets such as ships or complex buildings. As a kind of interference, it may reduce the image quality and hinder the image interpretation. Hence, range sidelobe suppression is an important mission for SAR images. The main task of mitigating the sidelobe is how to achieve the most effective suppression with the minimal resolution loss and signal-to-noise ratio (SNR) loss. However, the widely recognized classic method, spatially variant apodization (SVA), still has a lot of residual sidelobe energy and other problems. This article proposes a novel suppression approach based on time-variant transmission of chaotic frequency modulation (CFM) signals. The key is to build an appropriate transmitted signal set, where the signals are generated by various chaotic initial states and the same special map with low mixing rate and uniform invariant probability density (IPD). Due to their beneficial autocorrelation properties, the proposed approach achieves superior performance in range sidelobe suppression and resolution preservation. More importantly, it maintains the energy of the signals and overcomes the SNR loss that occurs in some classic methods, such as spectral weighting (SW) and SVA. In addition, it is suitable for both vertical and squint side-looking mode and can well reconstruct the weakly scattering targets which are severely disturbed by range sidelobe. All of them are validated by comparative experiments. [ABSTRACT FROM AUTHOR]

Azimuth ambiguity may introduce false targets into synthetic aperture radar images, particularly likely in inshore and oceanic observation. To suppress the azimuth ambiguities for any acquisition mode, a new model is developed to describe the impact of spatially variant azimuth antenna pattern weighting on azimuth ambiguities. By accurately estimating the ratio of ambiguous to main zone energy based on the model, the proposed algorithm selects the subspectra with less ambiguous disturbance, and adopts extrapolation with weighted energy measure to obtain a full spectrum. Due to spectral selection and extrapolation, the novel algorithm achieves superior performance in azimuth ambiguity suppression and resolution preservation, which is compared with the classical algorithm, and validated by applying TerraSAR-X and RADARSAT-2 images. [ABSTRACT FROM AUTHOR]