Unsupervised low-rank matrix decomposition (LRMD) theory, leveraging inherent structural features of images, has demonstrated significant potential for synthetic aperture radar (SAR) image change detection (CD) under label scarcity. However, conventional methods rely on static sparsity priors, which inadequately model the spatial diversity and dynamic complexity of changes, leading to great decomposition errors. To address this limitation, we redefine CD within a multiview framework and propose a structurally diversified self-representation learning model. By jointly enforcing low-rank and sparse constraints on the coefficient matrix, our approach enhances the global consistency and local continuity of change information, effectively rectifying representation errors in complex change patterns. Furthermore, a robust background suppression mechanism is integrated to mitigate speckle noise and pseudo-changes, improving the discriminability between changed and unchanged regions. To refine ambiguous boundaries, an unsupervised classification refinement module is developed, calibrating transitional samples via nonlinear regression-based feature projection without labeled data. The entire framework operates without supervision, eliminating dependence on annotated samples. Extensive experiments on six bitemporal and three extended multitemporal datasets validate the effectiveness and superiority of the proposed method. The source code will be made available athttps://github.com/95xiaoli/S3CD

In practical applications, airborne terahertz synthetic aperture radar (THz-SAR) echo signal is not only affected by range amplitude and phase errors introduced by non-ideal THz device, but also affected by motion error in azimuth induced by the non-ideal trajectory of platform, resulting in the two-dimensional (2-D) defocus of image. To address the above errors, a novel 2-D autofocusing algorithm for real airborne stripmap THz-SAR imaging is proposed. In range autofocusing, the amplitude and phase errors are estimated simultaneously in range frequency domain based on the dominant point target selected in the preliminary focused images. To avoid dividing more sub-apertures in azimuth error estimate, the autofocusing method based on the maximization of image contrast is adopted. Then, range-Doppler algorithm integrated with the proposed 2-D autofocusing are performed to obtain the focused images. The real measured data, acquired by a 0.22 THz airborne SAR system, are used to demonstrate the validity of the proposed algorithm. By comparing the imaging results, the proposed method can effectively and efficiently focus airborne stripmap THz-SAR images.

The functionalities, such as connectivity and communication capability of complex networks, are related to the number and length of paths between node pairs in the networks. In this paper, we propose a new path connectivity measure by considering the number and length of paths of the network (PCNL) to evaluate network path connectivity. By comparing the PCNL with the typical natural connectivity, we prove the effectiveness of the PCNL to measure the path connectivity of networks. Because of the importance of the shortest paths, we further propose the shortest paths connectivity measure (SPCNL) based on the number and length of the shortest paths. Then, we use edge-betweenness-based malicious attacks to study the relationship between the SPCNL and network topology in five types of networks. The results show that the SPCNLs of the networks have a significant corresponding relationship and similar changing trend with their network topology heterogeneities with the increase of the number of deleted edges. These findings mean that the SPCNL is positively correlated with the heterogeneity of the network topology, which provides a new perspective for designing complex networks with high path connectivity.

Defence against cascading failures is of great theoretical and practical significance. A novel load capacity model with a tunable proportion is proposed. We take degree and clustering coefficient into account to redistribute the loads of broken nodes. The redistribution is local, where the loads of broken nodes are allocated to their nearest neighbours. Our model has been applied on artificial networks as well as two real networks. Simulation results show that networks get more vulnerable and sensitive to intentional attacks along with the decrease of average degree. In addition, the critical threshold from collapse to intact states is affected by the tunable parameter. We can adjust the tunable parameter to get the optimal critical threshold and make the systems more robust against cascading failures.

In this paper, a high precision algorithm to estimate initial phase offset fast is presented for airborne dual-antenna InSAR system. Firstly, the factors influencing the initial phase offset are analyzed, and then the influence of the initial phase offset to DEM is given. According to the analysis, the algorithm of real-time estimation of the initial phase offset with high precision is presented. In order to evaluate the performance of the algorithm, two sets of real airborne dual-antennal InSAR data from the Institute of Electronics, Chinese Academy of Sciences (IECAS) are processed. The results prove the algorithm can get superior precision initial phase offset in real-time, which is very useful for real-time InSAR system.

In order to reduce the complex image registration process for the airborne dual-antenna interferometric SAR (InSAR) system, a new auto-registration imaging algorithm is presented in the paper, which implements the registration step directly at the SAR raw data processing stage based on the EOK algorithm and the ECS algorithm. For the mismatch caused by the path difference of two antennas, it realizes the precision registration of interferometric image in the range by means of high degree polynomial approximation using scaling principle and shifting compensation in course of the imaging procedure. Finally, through the processing of the real interferometric data, better interferograms are obtained and the validity of the algorithm proposed in the paper is proved.