When it comes to deep-learning-based methods for despeckling synthetic aperture radar (SAR) images, maintaining spatial structure, ensuring process stability, and achieving fast inference are still major challenges. To address these issues, a novel method named scattering guided image despeckling (SGID), which is based on the wavelet—transform—conditioned diffusion model for SAR despeckling, has been proposed. First, the core component of this approach is the adaptive scattering extraction module (ASEM), which is designed to preserve the spatial structure of images effectively. It achieves the preservation of spatial structure by leveraging SAR imaging principles to adaptively extract scattering points, thereby retaining critical spatial information. Second, considering that speckle noise in SAR images is mainly concentrated in the high-frequency region, this study has designed a high-frequency despeckling module (HFDM). This module adopts a direction-aware adaptive weight allocation strategy, aiming to precisely regulate the weight allocation of high-frequency components in all the directions during the processing, so as to maximize the coherence and consistency of the high-frequency detail structures in the images. Finally, the entire processing workflow is conducted in the frequency domain, which contributes significantly to the improvement of inference speed. Experimental results demonstrate that the proposed method achieves state-of-the-art (SOTA) performance in both spatial structure preservation and despeckling quality. Besides, its inference speed is 0.19 s, which is 67 times faster than that of comparable diffusion-model-based methods. The code for this study will be publicly available athttps://github.com/SihaoDong/SGID.

Although Convolutional Neural Networks (CNNs) have achieved success in traditional Closed-Set Recognition (CSR), their lack of robustness when faced with unknown categories in open environments is evident. In this context, Open Set Recognition (OSR) becomes particularly crucial, as it not only effectively classifies known categories but also accurately identifies unknown categories as "unknown," which is pivotal for the algorithm’s robustness in real-world applications. To address this challenge, we propose a prototype learning network based on attribute scattering center (ASC-CPL) to tackle open-set recognition issues in SAR image analysis. Firstly, this framework retains the representational capabilities of CNN while discarding the closed-world Softmax assumption, opting instead for an open-world and human-like prototype model. Secondly, to better handle the discreteness and sensitive pose characteristics of SAR image targets, this approach integrates attribute scattering centers into the convolutional kernel design, significantly enhancing the network’s ability to model scattering features. Experimental verification on the MSTAR dataset demonstrates the effectiveness and feasibility of this method, providing robust support for the future development of open-set recognition in the field.

Synthetic aperture radar (SAR) images are widely utilized for the detection and recognition of aircraft targets. Unlike optical images, SAR images possess the advantage of being applicable in all weather conditions and at all times of the day. However, in contrast to optical images, aircraft typically appear as discrete points in SAR images, and their outlines are not distinctly clear. To effectively use the scatter information of aircraft in SAR images, a convolutional modulation scattering feature network (CMSF) is proposed in this paper. Firstly, a scattering feature extraction module is introduced to make full advantage of scattering information. Secondly, following convolution processing, the convolution modulation module is employed to generate a similar fraction matrix. Thirdly, the fusion of scattering features and convolution features is achieved through convolutional modulation and matrix multiplication. Finally, a four-stage convolutional processing is employed to recognize the aircraft target. Extensive experiments conducted on the SARAircraft-1.0 dataset demonstrate the effectiveness of the convolutional modulated scattering feature network for aircraft target classification in SAR images.

The problem of insufficient samples has been limiting the performance of intelligent interpretation in Synthetic Aperture Radar (SAR) images. Humans have the ability to recognize new instances with only a few samples, indicating that attributes play a crucial role in recognition. Attributes can be shared across categories and provide a distinctive representation. Motivated by this fact, this paper proposes an attribute-guided network consisting of a base classifier (BC) and an attribute classifier (AC). Firstly, we design binary attributes for SAR objects to enable more distinct feature representations. Secondly, the images are mapped into a semantic embedding space by embedding the attribute vectors. Finally, the performance of few-shot classification in SAR images is improved by jointly optimizing the loss in both attribute space and deep feature space. Our attribute-based framework is validated through ablation experiments on the MSTAR dataset and a self-built SAR aircraft dataset.

Aircraft detection in synthetic aperture radar (SAR) image is a special case because all the targets are located in the airport. Comparing with the whole scene SAR image, the area of an airport is relatively small, therefore, this information can be utilized to speed up the algorithm. This paper proposes a centroid network detection method based on the combination of geographic coordinate information and subscene classification. Firstly, the airport area is detected based on the priori geographic coordinate information. Secondly, to further narrow down the scope of detection and extract the regions containing valid targets, the subscene is fed into the ResNet50 network which incorporates Squeeze and Excitation (SE) to separate the aircraft area from the background area. The method is validated in ablation experiments on the GaoFen-3(GF3) datasets to reveal the impact of each factor. The results show that the proposed method can achieve a reduction in false alarm rate around 6% and time cost around 30%.

With the continuous development of Deep Learning (DL), ship detection in SAR (Synthetic Aperture Radar) images based on convolutional neural networks (CNN) has become a common approach. CNNs with complex structures have achieved good performance in SAR images, but face challenges such as high time consumption and high false alarm rate, because of the sparsity of ships in the remote sensing images. In this paper, a rotated ship detection network based on the CFAR (Constant False Alarm Rate) preprocessing is proposed to address these problems. It first uses a CFAR preprocessing to fast narrow down the scope of detection so as to save processing time. Then, a classification network is designed to reduce the false alarm rate. The experiment results based on the Gaofen-3(GF-3) dataset show that the proposed method can reduce the false alarm rate greatly and use much less CPU time.