Due to its capability for all-weather, all-time information acquisition, Synthetic aperture radar (SAR) plays a vital role in the field of Earth observation. However, the specificity of the radar sensor and the complexity of electromagnetic scattering imaging physics result in SAR images lacking the intuitiveness of optical images, making them unsuitable for interpretation by non-experts. A common approach to tackle this challenge is to use conditional generative adversarial network (cGAN) to translate SAR images into optical images, thereby enhancing readability and assisting non-experts in interpretation while filling the gaps in optical data due to acquisition constraints. Nevertheless, traditional cGAN-based methods are limited by inadequate global semantic information extraction and poor detail preservation, leading to translated images with incoherent texture and color, and blurred edge. To address these issues, we propose a hybrid vision transformer conditional generative adversarial network (HVT-cGAN) for SAR-to-optical image translation (S2OIT). In our proposed HVT-cGAN, the generator utilizes a convolutional stem for patch embedding and encoding. The parallel CNN branch and vision transformer branch are employed for the extraction and mapping of local and global information, respectively. Moreover, we propose a novel attention-based feature fusion module, named the Convolutional Attention Fusion Module (CAFM), which can adaptively aggregate local and global information from parallel branches by learning both channel-wise and spatial-wise relations. Benefiting from these improvements, our method achieves superior performance in both qualitative and quantitative comparisons with other methods on the SEN1-2 dataset. Additionally, the results of multiple ablation experiments validate the effectiveness of the proposed method.

Compressed sensing-based synthetic aperture radar (SAR) sparse imaging technique has been widely used in SAR reconstruction tasks, but it is limited by high computational costs and poor imaging performance. In this letter, we propose a dynamic deep unfolding network based on content-aware shrinkage for SAR sparse imaging, called CAS-D²UN. First, an adaptive soft shrinkage mechanism is introduced to obtain content-aware threshold and enhance the adaptability of the model. Second, a dynamic path-controllable solution is proposed to adjust the network structure based on the complexity of the input data, thereby improving inference speed. Finally, experimental results demonstrate that our proposed CAS-D²UN can achieve superior sparse reconstruction for complex scenes while shortening the imaging time.

Autofocus plays a pivotal role in synthetic aperture radar (SAR) imaging, which is particularly crucial for high-resolution applications. Traditional autofocus methods face inherent limitations, such as reliance on strong isolated scatterers within the scene or prohibitive computational complexity, making them inadequate for the practical demands of the rapid and batch processing of massive SAR imagery. Although recent deep learning (DL)-based methods have improved focusing efficiency, they predominantly rely on predefined high-order polynomial models for phase error estimation, which inevitably introduces parameter redundancy, restricts the model’s adaptability to complex phase errors, and is highly prone to inducing severe oscillations at the aperture boundaries. To overcome these limitations, this article proposes DeepAutofocus, a non-parametric deep learning framework for SAR autofocus. First, we design a physics-inspired subaperture correlation Transformer architecture. A multiscale convolutional neural network (CNN) is utilized to extract local spatial features, which are then transformed into serialized subaperture image patches. Subsequently, the self-attention mechanism of the Transformer is employed to capture the long-range dependencies across subapertures. Second, we introduce a data-driven cubic B-spline reconstruction strategy that directly estimates phase errors without depending on predefined high-order polynomial models, thereby eliminating boundary oscillations while maintaining high flexibility. Extensive experiments on both simulated and real measured satellite datasets demonstrate that, compared to traditional autofocus algorithms and existing DL-based methods, the proposed DeepAutofocus achieves superior focusing performance under various complex phase error conditions.