Ship detection of synthetic aperture radar (SAR) images is one of the research hotspots in the field of marine surveillance. Fusing salient features to detection network can effectively improve the precision of ship detection. However, how to effectively fuse the salient features of SAR images is still a difficult task. In this paper, to improve the ship detection precision, we design a novel one-stage ship detection network to fuse salient features and deep convolutional neural network (CNN) features. Firstly, a saliency map extraction algorithm is proposed. The algorithm is applied to generate saliency map by using multi-scale pyramid features and frequency domain features. Secondly, the backbone of the ship detection network contains a two-stream network. The upper-stream network uses the original SAR image as input to extract multi-scale deep CNN features. The lower-stream network uses the corresponding saliency map as input to acquire multi-scale salient features. Thirdly, for integrating the salient features to deep CNN features, a novel salient feature fusion method is designed. Finally, an improved bi-directional feature pyramid network is applied to the ship detection network for reducing the computational complexity and network parameters. The proposed methods are evaluated on the public ship detection dataset and the experimental results shows that it can make a significant improvement in the precision of SAR image ship detection.

Computationally, the aesthetic quality of an image means that the model automatically scores the aesthetic level of the image. However, there are many factors that determine beauty or ugliness for photographic photos. Therefore, extracting a variety of representative aesthetic features and fusing these features are still difficult tasks. In this paper, we design a two-stream network to calculate the aesthetic quality of the image. The upper stream of the network is an improved network with the SEResNet-50 and six skip connections added, which can improve the performance of the model without training to obtain deep convolutional neural network features. The lower stream of the network consists of the proposed algorithms for handcrafted extracting aesthetic features and multiple convolution layers to extract the aesthetic features. Finally, to fuse the features of the two-stream network without adding feature dimensions, a novel feature fusion layer is proposed. The results show that this novel feature fusion method can calculate results close to the artificial aesthetic evaluation.

Synthetic aperture radar (SAR)¹ is an active Earth observation system deployed on aircraft, satellites, or other flight platforms. Compared with the optical and infrared systems, SAR can provide all-time, all-weather, high-resolution, and wide-swath observation. It also has a certain ability to penetrate the Earth’s surface and discover underground targets. Therefore, the SAR has advantages in disaster monitoring,² environmental monitoring,³ ocean surveillance,⁴ resource exploration,⁵ surveying, and military applications.

Self-supervised method has proven to be a suitable approach for despeckling on synthetic aperture radar (SAR) images. However, most self-supervised despeckling methods are trained by noisy-noisy image pairs, which are constructed by using natural images with simulated speckle noise, time-series real-world SAR images or generative adversarial network, limiting the practicability of these methods in real-world SAR images. Therefore, in this paper, a novel self-supervised despeckling algorithm with an enhanced U-Net is proposed for real-world SAR images. Firstly, unlike previous self-supervised despeckling works, the noisy-noisy image pairs are generated from real-word SAR images through a novel generation training pairs module, which makes it possible to train deep convolutional neural networks using real-world SAR images. Secondly, an enhanced U-Net is designed to improve the feature extraction and fusion capabilities of the network. Thirdly, a self-supervised training loss function with a regularization loss is proposed to address the difference of target pixel values between neighbors on the original SAR images. Finally, visual and quantitative experiments on simulated and real-world SAR images show that the proposed algorithm notably removes speckle noise with better preserving features, which exceed several state-of-the-art despeckling methods.

Shadow and layover are geometric distortion phenomenons in side-view imaging synthetic aperture radar (SAR) systems, especially in mountainous areas and densely populated urban areas. The shadow can block the target of the observation area, making it impossible to obtain the scattering characteristics of the target. The layover causes phase distortion and alters target characteristics. Shadow and layover severely hinder the interpretation of SAR images. To confront the above problems, a multi-angle fusion algorithm based on unsupervised progressive segmentation network is proposed. Firstly, inspired by mega-constellations of low earth orbit, a spaceborne SAR collaborative observation model is proposed to generate multi-angle images of fluctuant terrain. Secondly, according to the difference of echos in the shadow and layover regions, an unsupervised progressive segmentation network is designed to sequentially segment the shadow and layover regions. Finally, to improve the contrast and brightness of the fused SAR image, a single-scale weighted fusion algorithm is designed. Experiments were conducted using the simulated multi-angle SAR images. Compared with single-angle images, the accuracy of target detection and figure-of-merit of the fused SAR image are significantly higher than those of other methods.

A new model is presented to resolve cycle slips detection for triple-frequency observations of BeiDou navigation satellite system (BDS) in this paper when pseudorange observations are missing or insufficiently accurate under harsh or special situations. Based on the first-order time-difference geometry-free (GF) pseudorange-phase combination model, the new cycle slips detection and correction method based on the triple-frequency carrier phase and Doppler observations is proposed. With analyses on the two common sampling intervals (30 s and 1 s), it can be concluded that the optimal combination coefficients of the proposed model relate to sampling intervals. Combinations [4,-2,-3], [-1,-5,6], and [-3,6,-2] are selected to detect and correct cycle slips for 30 s sampling interval, while combinations [0,-1,1], [1,0,-1], and [-3,2,2] are selected for 1 s sampling interval. The validity of the phase-Doppler combination model under the static condition and the steady ionosphere with 30 s sampling interval and 1 s sampling interval is verified by two static experiments. Results show that the phase-Doppler combination model can achieve the same performance as the pseudorange-phase combination model. All the small, insensitive, and large cycle slips added to the three types of BDS satellites which separately belong to Geostationary Earth Orbit (GEO), Inclined Geosynchronous Orbit (IGSO), and Medium Earth Orbit (MEO) are detected and corrected successfully by the proposed model.