This work introduces DiSpeckle, a diffusion-based SAR despeckling method in which a physics-inspired stochastic differential equation (SDE) governs the bidirectional transition process between speckled and clean images. The key insight is that the forward diffusion process closely parallels the physical formation of speckle noise—both involve the accumulation of microscopic random perturbations that progressively distort the underlying signal. Building on this analogy, the forward diffusion process is designed using an SDE that emulates the speckle formation mechanism, namely the coherent aggregation of unresolved backscattered signals. Despeckling is then achieved by inverting this process through the corresponding reverse SDE. Unlike previous diffusion-based methods that rely on transforming speckle into an approximately Gaussian distribution, this physics-inspired formulation offers a mathematically rigorous and physically consistent pathway from speckled to clean imagery. In addition, DiSpeckle provides a flexible framework that seamlessly integrates off-the-shelf Gaussian denoisers. Experiments demonstrate that DiSpeckle—even when using off-the-shelf Gaussian denoisers—matches or outperforms state-of-the-art despeckling methods. With fine-tuned denoisers, it achieves superior performance while requiring only one-third of the training data. The code will be made publicly available on GitHub upon publication.

The active jamming suppression of Synthetic Aperture Radar (SAR) images remains a severe challenge. The jamming SAR images simulation dataset is firstly built by open SSDD dataset and random shift-frequency jamming type; The formula of existing diffusion model is then modified using the low-rank based block space filter (BSF) theory; Lastly, jamming SAR images are as the inputs to effectively train our proposed model to generate the jamming suppressed images. Experimental results qualitatively and quantitatively demonstrate the effectiveness of the proposed method.

SAR despeckling is a crucial preprocessing step for various SAR applications. This work introduces SpeckleDiff2D, a complex-valued speckle reduction diffusion network. Our approach emphasizes the design of a forward noise-accumulation process that mimics the generation mechanism of speckle noise. The proposed despeckling algorithm is trained exclusively on synthesized speckled images generated by optical dataset and is validated on both simulated and real SAR images. Experimental results demonstrate the method’s efficiency and resilience to domain shifts, positioning it as a promising solution to the persistent challenge of data insufficiency in SAR despeckling design.

Multi-band polarimetric synthetic aperture radar (PolSAR) has significant advantage in information extraction. However, the demanding acquisition requirement greatly prohibits its development. Typically, compared to low-frequency band PolSAR data, high-frequency band suffers more severe data insufficiency. In this paper, the authors proposed to resolve this issue by simulating Ka-band PolSAR images from X-band images. For this purpose, a conditional Generative Adversial Network (cGAN) based X-to-Ka band PolSAR image transfer network has been proposed. Adaptations in terms of preprocessing and loss function are made to the original cGAN so that it can be better adapted to PolSAR image processing. The proposed method is verified using the X- and Ka-band dataset acquired in Hainan, China by the Aerial Remote Sensing System of the Chinese Academy of Sciences. Experimental results demonstrate the feasibility of the proposed method.

Data insufficiency poses a significant challenge in Ka-band Polarimetric Synthetic Aperture Radar (PolSAR) applications. Traditional PolSAR simulation approaches fail to conquer this issue due to the intricate modeling and computational complexities induced by high-frequency. In this paper, the authors propose to mitigate this issue through neural style transfer. An X2Ka translation network is proposed to transfer X-band PolSAR images to Ka-band. Leveraging the well-verified generative network Pix2Pix, the authors adapt it to accommodate the specific discrepancies between PolSAR and optical data. Experiments are conducted on X- and Ka-band PolSAR images acquired by an Airborne PolSAR system from the Chinese Academy of Sciences. Both qualitative and quantitative evaluation results demonstrate the effectiveness of the proposed network.

This letter proposes a novel convolutional neural network (CNN) method for dual-polarized synthetic aperture radar (SAR) ship grained classification. The network employs hybrid channel feature loss that jointly utilizes the information contained in the polarized channels (VV and VH). It is demonstrated that, by adopting the proposed CNN framework and the novel loss function, the classification performance can be efficiently improved. First, instead of the prevalently used threefold or fourfold division (container ship, oil tanker, bulk carrier, and so on), the proposed method can further divide vessels into eight accurate categories. Second, this method can not only effectively classify targets into eight categories but also its accuracy in terms of fewer category classifications surpasses existing methods. Third, the method can achieve good performance on a small training data set. Experiments conducted on the OpenSARShip data sets indicate that the proposed classification method achieves state-of-the-art results.