Compared with conventional dynamic nonlinear equation systems, a hybrid double-deck dynamic nonlinear equation system (H3DNES) not only has multiple layers describing more different tasks in practice, but also has a hybrid nonlinear structure of solution and its derivative describing their nonlinear constraints. Its characteristics lead to the ability to describe more complicated problems involving multiple constraints, and strong nonlinear and dynamic features, such as robot manipulator tracking control. Besides, noises are inevitable in practice and thus strong robustness of models solving H3DNES is also necessary. In this work, a multilayered noise-tolerant zeroing neural network (MNTZNN) model is proposed for solving H3DNES. MNTZNN model has strong robustness and it solves H3DNES successfully even when noises exist in both the two layers of H3DNES. In order to develop the MNTZNN model, a new zeroing neural network (ZNN) design formula is proposed. It not only enables equations with respect to solutions to become equations with respect to the second-order derivatives of solutions but also makes the corresponding model have strong robustness. The robustness of the MNTZNN model is proved when parameters in the model satisfy a loose constraint and the error bounds are programmable via setting appropriate parameter values. Finally, the MNTZNN model is applied to the tracking control of the six-link planar robot manipulator and PUMA560 robot manipulator with hybrid nonlinear constraints of joint angle and velocity.

With the expansion of optical and SAR image fusion application scenarios, it is necessary to integrate their information in land classification, feature recognition, and target tracking. Current methods focus excessively on integrating multimodal feature information to enhance the information richness of the fused images, whereas neglecting the highly corrupted visual perception of the fused results by modal differences and SAR speckle noise. To address that, this paper proposes a novel optical and SAR image fusion framework named Visual Saliency Features Fusion (VSFF), which is based on the extraction and balancing of significant complementary features of optical and SAR images. Firstly, we propose a decomposition algorithm of complementary features to divide the image into main structure features and detail texture features. Then, for the fusion of main structure features, we reconstruct the visual saliency features maps of the pixel and structure that contain significant information from optical and SAR images, and input them into a total variation constraint model to compute the fusion result and achieve the optimal information transfer. Meanwhile, we construct a new feature descriptor based on the Gabor wavelet, that separates meaningful detail texture features from residual noise and selectively preserves features that can improve the interpretability of fusion result. In a comparative analysis with seven state-of-the-art fusion algorithms, VSFF achieved better results in qualitative and quantitative evaluations, and our fused images have a clear and appropriate visual perception. The source code is publicly available at https://github.com/yeyuanxin110/VSFF.

Void underneath a slab is a common damage type in airport cement concrete pavement. Existing methods mainly use ground penetrating radar (GPR) images and deep learning to detect the voids but face two problems: inherent limitation of image-wise deep learning and imperfect GPR datasets. This study has proposed a signal-wise cascade deep network (SWC-net) to detect voids underneath slabs, which was trained by a dataset with real-world and simulation GPR signals. In the study, a multi-phase heterogeneous pavement model was first built, in which each pavement layer was reconstructed based on the real-world morphologies and distributions of aggregates and other materials. The model was then used for the finite-difference time-domain (FDTD) simulation of GPR signal propagation. The simulation signals were merged with real-world GPR signals to generate a void signal dataset covering comprehensive pavement conditions. Finally, the proposed SWC-net was trained by the merged dataset to detect voids. The inputs of the network were the GPR signals, while the outputs were the signal classes and abnormal intervals of voids to perform signal-wise void detection. The experiments on Nanjing Dajiaochang and Xuzhou Guanyin airports showed that the FDTD results generalized the signal dataset by simulating different real-world conditions, while the proposed network outperformed the image-wise deep networks on void detection.

Establishing feature correspondences between multimodal remote sensing images is an essential task for realizing diverse applications. Conventional matching methods, which employ gradient or phase congruency (PC) for feature detection and description, produce limited performance when images suffer from strong noises and intensity differences. In this study, we propose a novel modality-invariant structural feature representation (MISFR) method for multimodal remote sensing image matching. First, a maximal/minimal enhanced PC moment (EPCM) representation is designed by incorporating the PC with a multiscale relative total variation (RTV) model for feature detection and description. The EPCM integrates the advantages of these two models to exploit intrinsic structural features while providing robustness against modality variations. Then, to improve the feature stability and repeatability, an aggregation structural feature detector (ASFD) is proposed, in which the corner and edge points are separately extracted on the minimal and maximum EPCMs. Moreover, an adaptive binning-based log-polar descriptor is constructed on the maximal EPCM, named enhanced features of orientated PC (EOPC), to robustly characterize feature points. Various experiments on a series of multimodal remote sensing images demonstrate that our MISFR significantly improves the matching performance compared with four state-of-the-art approaches.

The structural features using self-similarity have become more popular for multimodal remote sensing image matching. However, mostly because of significant geometric distortions and nonlinear intensity differences between images, these methods produce a limited matching performance when directly applied to multimodal remote sensing images. To address that, we propose a novel feature descriptor named pyramid features of orientated self-similarity (POSS) for multimodal remote sensing image matching, which integrates phase congruency (PC) into the self-similarity model for better encoding structural information. Unlike these conventional self-similarity-based descriptors, the POSS is constructed by using PC instead of image intensity in a pyramid manner and thus has better discriminability and robustness to modality variations. In addition, we design a uniform multimoment of PC (UMMPC) detector for feature detection, which can improve the number, distribution, and repeatability of feature points. Experimental results demonstrate its superior applicability of the proposed method over the state-of-the-art methods, showing much better matching performance.

Deep learning-based change detection (CD) using remote sensing images has received increasing attention in recent years. However, how to effectively extract and fuse the deep features of bi-temporal images for improving the accuracy of CD is still a challenge. To address that, a novel adjacent-level feature fusion network with 3D convolution (named AFCF3D-Net) is proposed in this article. First, through the inner fusion property of 3D convolution, we design a new feature fusion way that can simultaneously extract and fuse the feature information from bi-temporal images. Then, to alleviate the semantic gap between low-level features and high-level features, we propose an adjacent-level feature cross-fusion (AFCF) module to aggregate complementary feature information between the adjacent levels. Furthermore, the full-scale skip connection strategy is introduced to improve the capability of pixel-wise prediction and the compactness of changed objects in the results. Finally, the proposed AFCF3D-Net has been validated on the three challenging remote sensing CD datasets: the Wuhan building dataset (WHU-CD), the LEVIR building dataset (LEVIR-CD), and the Sun Yat-Sen University dataset (SYSU-CD). The results of quantitative analysis and qualitative comparison demonstrate that the proposed AFCF3D-Net achieves better performance compared to other state-of-the-art methods. The code for this work is available at https://github.com/wm-Githuber/AFCF3D-Net.

Identifying feature correspondences between multimodal images is facing enormous challenges because of the significant differences both in radiation and geometry. To address these problems, we propose a novel feature matching method (named R2FD2) that is robust to radiation and rotation differences, which consists of a repeatable feature detector and a rotation-invariant feature descriptor. In the first stage, a repeatable feature detector called the Multi-channel Auto-correlation of the Log-Gabor (MALG) is presented for feature detection, which combines the multi-channel auto-correlation strategy with the Log-Gabor wavelets to detect interest points (IPs) with high repeatability and uniform distribution. In the second stage, a rotation-invariant feature descriptor is constructed, named the Rotation-invariant Maximum index map of the Log-Gabor (RMLG), which includes fast assignment of dominant orientation and construction of feature representation. In the process of fast assignment of dominant orientation, a Rotation-invariant Maximum Index Map (RMIM) is built to address rotation deformations. Then, the proposed RMLG incorporates the rotation-invariant RMIM with the spatial configuration of DAISY to improve RMLG’s resistance to radiation and rotation variances. Finally, we conduct experiments to validate the matching performance of our R2FD2utilizing different types of multimodal image datasets. Experimental results show that the proposed R2FD2outperforms five state-of-the-art feature matching methods. Moreover, our R2FD2achieves the accuracy of matching within two pixels and has a great advantage in matching efficiency over contrastive methods.

Due to the significant differences in geometric and nonlinear intensity, multimodal image matching is still a challenging problem. To address this issue, this paper proposes a novel matching method using phase congruency (PC)-based self-similarity structural features for multimodal images. Firstly, the feature points are extracted from the PC maps of the original images by the Harris detector. Then, combined with the theory of the self-similarity, a PC-based self-similarity structural (PCSS) descriptor is designed for multimodal images. Finally, the Euclidean distance is used as the matching measure for the corresponding point recognition. Experimental results conducted on various real multimodal image pairs demonstrate that the proposed method can achieve better matching performance in terms of the number of correct matches and the registration precision in comparison with the traditional methods.

Automatic matching of synthetic aperture radar (SAR) and optical images is a fundamental task in many remote sensing applications. However, due to different imaging modalities, conventional matching methods provide limited performances. In this letter, based on the observation that structural features are maintained across different modality images, we propose a novel feature-based method to effectively address SAR and optical image matching. The proposed method is built on the phase congruency (PC) model and consists mainly of two stages. First, a modified version of the uniform nonlinear diffusion-based Harris (MUND-Harris) detector is introduced to extract the local features. Unlike the UND-Harris, MUND-Harris employs the PC instead of image intensity for feature extraction and thus obtain well-distributed and highly repeatable feature points. Second, a local structural descriptor, namely PC order-based local structural (PCOLS), is designed for the extracted points. PCOLS is constructed in a grouping manner and further encodes image structures with an adaptive descriptor structure, which provides robustness against modality variations including significant geometric and intensity differences. Experimental results obtained on several SAR and optical image pairs demonstrate the encouraging performance of the proposed method.

Automatic and precise matching between optical and synthetic aperture radar (SAR) images is still a challenging task because of significant radiation and texture differences between such images. Recently, structure feature-based methods are popular for the matching of SAR and optical images. However, current structure descriptors include many noninformative features, which degrade their matching performance. To address that, we present a robust matching method by a multiscale masked structure feature representation. We first extract pixelwise gradient structure features on multiple scales of images. Then, a mask is constructed according to large contours of an image, which is used to increase the contribution of the main structure region and alleviate the influence of noninformative regions. Finally, a fast template scheme based on fast Fourier transform (FFT) is employed to obtain correspondences. The proposed method is tested using the optical and SAR images from the Sentinel and GaoFen sensors. Experiment results show that the proposed method significantly improves the matching performance compared with the state-of-the-art methods, especially for the images with poor structure features.

Automatic registration of synthetic aperture radar (SAR) and optical images is still a challenging problem because of the potential differences in geometric and intensity. In this work, we propose a robust and efficient method for improving the registration performance of SAR and optical images. Our work consists mainly of two steps, including the feature point detection stage and the feature description stage. In the first stage, we present a new feature extraction method, named nonlinear diffusion-based Harris-Laplace (NDHL) detector, which incorporates the nonlinear diffusion and a spatial consistency strategy into the Harris-Laplace detector, which can weaken the modality variations and enhance the structural features of the multimodal images, and can detect many more similar and highly repeatable feature points. In the second stage, we design a novel structural descriptor, named multiscale adaptive binning phase congruency (MABPC). The proposed MABPC descriptor encodes multiscale phase congruency features with an adaptive binning spatial structure, which brings an improvement of the robustness against geometric and nonlinear intensity discrepancies. Experimental results on both simulated and real image pairs show that the proposed registration method achieves encouraging performance improvements over other state-of-the-art methods. In comparison with the ROS-PC and RIFT methods, the proposed method obtains an average 54.6% improvement for the number of correct matches and has an average registration precision of 1.38 pixels.

Network Function Virtualization (NFV) can provide the resource according to the request and can improve the flexibility of the network. It has become the key technology of the Internet of Things (IoT). Resource scheduling for the virtual network function service chain (VNF‐SC) is the key issue of the NFV. Energy consumption is an important indicator for the IoT; we take the energy consumption into the objective and define a novel objective to satisfying different objectives of the decision‐maker. Due to the complexity of VNF‐SC deployment problem, through taking into consideration of the heterogeneity of nodes (each node only can provide some specific VNFs), and the limitation of resources in each node, a novel optimal model is constructed to define the problem of VNF‐SC deployment problem. To solve the optimization model effectively, a weighted center opposition‐based learning is introduced to brainstorm optimization to find the optimal solution (OBLBSO). To show the efficiency of the proposed algorithm, numerous of simulation experiments have been conducted. Experimental results indicate that OBLBSO can improve the accuracy of the solution than compared algorithm.

Cellular vehicle-to-everything (C-V2X) communication, as a part of 5G wireless communications, has been considered one of the most significant techniques for Smart City. Vehicles platooning is an application of Smart City that improves traffic capacity and safety by C-V2X. However, different from vehicles platooning travelling on highways, C-V2X could be more easily eavesdropped and the spectrum resource could be limited when vehicles converge at an intersection. Satisfying the secrecy rate of C-V2X, how to increase the spectrum efficiency (SE) and energy efficiency (EE) in the platooning network is a big challenge. In this paper, to solve this problem, a Security-Aware Approach to Enhancing SE and EE Based on Deep Reinforcement Learning is proposed, named SEED. The SEED formulates an objective optimization function considering both SE and EE, and the secrecy rate of C-V2X is treated as a critical constraint of this function. The optimization problem is transformed into the spectrum and transmission power selections of V2X links using deep Q network (DQN). The heuristic result of SE and EE is obtained by the DQN based on rewards mechanism. Finally, the traffic and communication environments are simulated by Python 3. The evaluation results demonstrate that the SEED outperforms the DQN-wopa algorithm and the baseline algorithm by 31.83% and 68.40% in efficiency, respectively.

With the advance of urban railway transportation and wireless communication technology, an increasing concern is placed on the reliability and low latency of urban mass transit communication system. Nowadays, both of WLAN and LTEM systems, known as two mainstream subway communication systems, are deployed redundantly with single communication network in case of unexpected failure. However, using a single communication system has irresistibility to frequency interference and traditional redundant cold backup manual has an intolerable delay in switching. In this paper, we adopt a multipath transmission network architecture with heterogeneous communication systems to solve the problems mentioned above. To realize the hot backup networks, the Redundant Forward Gateway (RFG) device is designed to connect terminals to the two networks. Information forwarded by source RFGs can be transmitted redundantly and simultaneously via both WLAN and LTE-M communication systems. At the other side, the redundant data packets will be removed before being forwarded to the destination terminals. Besides, tests were performed in various communication scenarios to evaluate the availability and effectiveness of the RFG and the network architecture. The results indicate that the RFG has a negligible processing delay and improves the robustness and performance compared to the traditional LTE-M network according to the transmission delay and packet loss tests. In conclusion, it is considered that the RFG and the heterogeneous LTE-M and WLAN networks are of vital importance in enhancing reliability in the metro communication system.

The registration of synthetic aperture radar (SAR) and optical images is a challenging task due to the potential nonlinear intensity differences between the two images. In this paper, a novel image registration method, which combines nonlinear diffusion and phase congruency structural descriptor (PCSD), is proposed for the registration of SAR and optical images. First, to reduce the influence of speckle noise on feature extraction, a uniform nonlinear diffusion-based Harris (UND-Harris) feature extraction method is designed. The UND-Harris detector is developed based on nonlinear diffusion, feature proportion, and block strategy, and explores many more well-distributed feature points with potential of being correctly matched. Then, according to the property that structural features are less sensitive to modality variation, a novel structural descriptor, namely, the PCSD, is constructed to robustly describe the attributes of the extracted points. The proposed PCSD is built on a PC structural image in a grouping manner, which effectively increases the discriminability and robustness of the final structural descriptor. Experimental results conducted on SAR and optical image pairs demonstrate that the proposed method is more robust against speckle noise and nonlinear intensity differences and improves the registration accuracy effectively.

Image registration is an important preprocessing step in many synthetic aperture radar (SAR) image applications. A key issue in image registration is to reliably establish the correspondences between the feature points extracted from the reference and sensed images. A new point matching algorithm is proposed in this paper to align two SAR images. In the proposed method, by considering image patches as the basic units, a novel local descriptor including the intensity and geometric information is assigned to each feature point, which is more robust to speckle noise. Furthermore, a correspondence establishment scheme is introduced based on the reconstruction errors between feature points calculated by the sparse representation (SR) technique, which is designed for achieving accurate matches. Based on the obtained SR coefficients, a coordinate correction procedure is further proposed for improving the localization accuracy of the obtained correspondences. Both simulated deformed and real SAR images are utilized to evaluate the performance. The experimental results indicate that the proposed method yields a better registration performance in terms of both accuracy and robustness.

Currently, salient-region detection, which can be used to provide important man-made target information for synthetic aperture radar (SAR) image analysis and interpretation, is a valuable tool in SAR image processing. However, because SAR images are blurred by a large amount of multiplicative speckle noise, it is difficult for existing methods to produce satisfying results with simple intensity variation. Based on the anisotropic scale space, which can describe edge variations, this letter presents a new salient-region detection method for intensity SAR images. In this method, by using the constant false alarm rate operator of the statistical distribution of the intensity, the edge intensity of each scale is measured with a corresponding-scale window to create the anisotropic scale space. Then, the dual Kullback-Leibler divergence between adjacent scales is computed to obtain the saliency metric of the pixel at a salient scale. To select salient regions, a simple iterative algorithm is presented. Experiments demonstrate the descriptive capacity of our anisotropic scale space and the performance of our method for intensity SAR images.

The scale-invariant feature transform (SIFT) algorithm has been widely applied to optical image registration. However, mostly because of multiplicative speckle noise, SIFT has a limited performance when directly applied to synthetic aperture radar (SAR) image. In this letter, a novel SAR image registration method is proposed, which is based on the combination of SIFT, nonlinear diffusion, and phase congruency. In our proposed algorithm, the multiscale representation of a SAR image is generated by nonlinear diffusion, since it better preserves edges in the image as opposed to Gaussian smoothing, which is used in the original SIFT. To reduce the influence of multiplicative speckle noise, the ratio of exponential weighted average operator is used to compute the gradient information in the construction of nonlinear diffusion scale space. Moreover, phase congruency information is utilized to remove the erroneous keypoints within the initial keypoints. Experimental results on multipolarization, multiband, and multitemporal SAR images indicate that our algorithm can improve the match performance compared to the SIFT-based method, which leads to a subpixel accuracy for all the tested image pairs.

The recently proposed triplet Markov random fields (TMF) model is very suitable for dealing with non‐stationary image segmentation. However, influenced by multiplicative speckle noise, synthetic aperture radar image (SAR) is dim and blurred in the boundaries of different areas, making it difficult to locate boundary accurately in the segmentation process. Thus, in this study, the authors propose a new segmentation algorithm using fuzzy label field‐based TMF model for SAR images. In the proposed algorithm, the value of each site in the label field is extended from a finite discrete set in the classical TMF model to a continuous one, in order to describe the memberships of each pixel to different classes. A fuzzy energy function is constructed to describe the joint prior distribution of the fuzzy label field and the auxiliary field. The construction of fuzzy energy function also takes into account four direction information and degree of difference between neighbouring pixels. Iterative conditional estimation method and maximum posterior mode criterion are applied to implement parameter estimation and segmentation. Experimental results on simulated data and real SAR images demonstrate the effectiveness of the proposed algorithm.

The salient region, which is a basic feature in the early stage of the human vision system, has been utilized to solve the problems of image analysis and interpretation nowadays. Although there are several salient-region detection methods for optical images, it is a hard work for synthetic aperture radar (SAR) images, which has large multiplicative speckle noise. Based on the statistical distribution of speckle noise and the local intensity variation, this letter presents a novel multiple-scale salient-region detection method for intensity SAR images. In this method, via constructing a 2-D local-intensity-variation histogram, the self-dissimilarity metric curve over scale is computed first to determine the saliency of the local region and its salient scale. Then, based on the Gamma statistical distribution of speckle noise, a new local complexity metric is proposed to obtain the saliency metric at the salient scale. After collecting all the salient regions in the image, a simple iterative algorithm is presented to refine the stable salient regions. Experimental results show the noise robustness, the accuracy, and the stability of the proposed method for SAR images.