With the significant advancements in deep learning technology and the substantial improvement in remote sensing image resolution, remote sensing semantic segmentation has garnered widespread attention. Synthetic aperture radar (SAR) and optical images are the primary sources of remote sensing data, offering complementary information. SAR images can capture surface information even under cloud cover and at night, whereas optical images provide higher resolution in clear weather conditions. Deep learning-based feature fusion methods can effectively integrate multisource information to obtain more comprehensive surface data. However, there are significant spatiotemporal differences in multisource information, making it challenging to select and extract the most discriminative features for segmentation tasks. To address this, we propose a lightweight and efficient fusion semantic segmentation network, SOLSTM, which mixes SAR and optical images as inputs and performs cyclic cross-fusion to establish a new network paradigm. To tackle multisource data heterogeneity, we introduce SAR-OPT matching attention, which aggregates multisource image features by adaptively adjusting fusion weights, thereby achieving comprehensive perception of feature channels and contextual information. Additionally, to mitigate the high computational complexity of processing multidimensional data, we introduce the mLSTM block, which employs linear operations to mine global contextual information in fused images, thus reducing computational complexity and enhancing image segmentation performance. Experiments on the WHU-OPT-SAR dataset show that SOLSTM has excellent performance, achieving up to 52.9 mIoU and outperforming single source image segmentation, verifying the effective fusion of OPT-SAR.

Detecting ships in Synthetic Aperture Radar (SAR) images is a challenging task due to various factors, such as the diverse distribution of ships and the intricate nature of SAR images. In recent years, deep learning has made excellent progress in the field of SAR interpretation. Models that focus on extracting global semantic information can effectively achieve balanced detection of multi-scale SAR targets, but their computational complexity is relatively high. Models that focus on processing local information have redundant calculations and poor robustness, but are prone to mistaking the background information of SAR images for targets. To address the above issues, we propose a real-time SAR ship detector based on global semantic information enhancement and local gradient information mining. The lightweight feature extraction backbone based on linear computing is designed, with the network structure of Global Information Augmentation Encoder (GIAE)—Local Gradient Information Miner (LGIM)—Decoder, which can quickly perform feature extraction. GIAE enhances the expression of image content through the long sequence modeling capability of the State Space Model. LGIM uses gradient modules composed of depthwise separable convolutions to extract local information of image, and utilizes directed self-attention (DSA) to mine channel context information. GLDet can complete object detection, rotated object detection and instance segmentation tasks by transforming the detection head. Excellent performance has been achieved on the SAR ship instance segmentation dataset SSDD and HRSID, as well as the SAR rotated ship dataset RSDD-SAR and SSDD+. Meanwhile, GLDet demonstrated excellent generalization performance in large-scale SAR images captured by GF-3 and Terra-SAR satellites.

With the rapid development of Synthetic Aperture Radar (SAR), the number and resolution of SAR images are constantly increasing. As a high-value target, aircraft detection has become a research hotspot in the field of SAR image interpretation. SAR aircraft have diverse postures, complex backgrounds, and small differences among different types of aircraft, which can easily lead to false detections. Meanwhile, some SAR aircraft have incomplete structures and are accompanied by speckle noise, which can easily lead to missed detections. To address the above issues, we propose an ultra-lightweight SAR aircraft detection network based on multi-scale matching attention (MSMANET). Firstly, we propose an ultra-lightweight backbone that extracts SAR gradient features through parallel processing of traditional convolution and Ghost modules. Secondly, aiming to the scale, shape and background information of aircraft, Multi-Scale Matching Attention (MSMA) is designed. MSMA performs feature aggregation and cross channel feature matching on multi receptive field feature maps, making the network more focused on feature maps suitable for detection. The mean average precision (mAP) of MSMANET on the SAR-AIRcraft1.0 dataset is as high as 98.4%, with the 1.6 GFLOPS, 657K parameter and 55.1 FPS. Compared to existing advanced networks, the performance has reached SOTA.

The scattering center model had been proved to be able to characterize the scattering fields from an extended target with high precision. Besides radar image interpretation and radar characteristics recognition, the application of scattering center model is extended to the computation of linear glint errors in this paper. The formula of linear glint errors are derived, as well as the relationship of linear glint errors at two orthogonal directions, which are of significance for the study of probability distribution functions of linear glint errors. The analytical results are validated by the results of the traditional method using the data bank of scattered fields.

It has been proved that scattering center modeling for narrow band signals can be achieved through the optimum image matching of time-frequency representation (TFR). In order to obtain TFR, however, the azimuth sampling interval should be quite small for electrically large target, which results in a huge amount of electromagnetic computation. Under the situation of under-sampling scattered waves, aliasing distortions of the Doppler curves of scattering centers will be caused in TFR, which makes it hard to estimate the parameters of corresponding scattering centers from TFR. To deal with this problem, an approach for scattering center modelling from under-sampling scattered waves are presented in this paper. The random under-sampling scattered waves based on the principle of compressive sensing are applied to acquire the non-aliasing Doppler curves of scattering centers in TFR. The feasibility of this method has been validated by the simulation results.

Summary Cloud computing is a novel paradigm capable of rationalizing the use of computational resources by means of outsourcing and virtualization. Elasticity is one of the most attractive features of cloud computing. Elastic clouds are able to adapt to workload changes by provisioning and de‐provisioning resources in an autonomic manner, such that at each point in time the available resources match the current demand as closely as possible. However, elasticity adds complexity, which makes quantitative analysis of cloud performance and power consumption difficult. Such analysis is required to evaluate and quantify the cost‐benefit of a strategy portfolio and the quantitative runtime performance and power consumption experienced by cloud‐users. In this study, we present a comprehensive analytical approach to performance and power consumption analysis of elastic clouds. Several metrics are defined and evaluated: expected task completion time, power consumption rate, and task rejection rate under different load conditions, elasticity intensities, and error intensities. To validate the proposed approach, we obtain experimental data through a real‐world cloud and conduct a confidence interval analysis. The analysis results suggest the perfect coverage of theoretical results by corresponding experimental confidence intervals. Copyright © 2016 John Wiley & Sons, Ltd.

Millimeter-wave (MMW) radar, which is used for road feature recognition, has performance that is superior to optical cameras in terms of robustness in different weather and lighting conditions, as well as providing ranging capabilities. However, the signatures of road features in MMW radar images are quite different from that of optical images, and even physically continuous features, such as road edges, will be presented as a set of bright points or spots distributed along the roadside. Therefore, discrimination of the radar features is of paramount importance in automotive imaging systems. To tackle this problem, an approach called the stripe Hough transform (HT) is introduced in this paper, allowing enhanced extraction of the geometry of the road path. The performance of the approach is demonstrated by comparison of extracted features from MMW images with the real geometry of the road and with the results of processing by classical HT.