Multi-dimensional opinion dynamic models can often be used to describe the mutual influence of agents on different topics expressed in social networks. This paper mainly focuses on the multi-dimensional opinion dynamics on social networks with asynchronous updates, where networks include two types of agents: stubborn agents and non-stubborn agents. In the asynchronous update mechanism, the update time instants of each agent are independent and different from those of other agents, which leads to the complexity and time variance of opinion interaction among agents. This paper comprehensively analyzes the convergence of multi-dimensional opinion dynamic models under asynchronous updates, relying on the techniques of binary relation composition and infinite products of sub-stochastic matrices. The results show that the opinions of stubborn agents gradually influence the opinions of non-stubborn agents, and ultimately completely determine the stable opinions of non-stubborn agents. The theoretical results are validated through numerical simulations.

This paper investigates the distributed localization problem in wireless sensor networks by adopting the barycentric coordinate method based on angle measurement. First, all sensor nodes are divided into two categories: anchor nodes with known locations and non-anchor nodes with unknown locations. On this basis, each non-anchor node calculates its barycentric coordinates relative to its neighbor nodes through angle measurement technology, and constructs an iterative equation for location estimation based on the local information exchange mechanism. Subsequently, corresponding localization algorithms are designed for two typical network deployment scenarios: non-anchor nodes are distributed inside the convex hull of anchor nodes and randomly distributed outside the convex hull. By introducing the convergence analysis method of sub-stochastic matrix multiplication, it is theoretically proved that the proposed distributed iterative localization algorithm can achieve progressive and precise localization of non-anchor nodes in both above two network deployment scenarios. Finally, the effectiveness of the proposed method is verified by numerical simulation experiments. The results show that the method can achieve high localization accuracy in both above two types of sensor network structures.

As urbanization accelerates, more and taller buildings and less greenery are closely related to changes in the urban thermal environment (UTE). Knowledge of spatial and temporal variations of UTE is becoming increasingly concerning, and this can be measured with the land surface temperature (LST). Satellite observation of LST is an important tool for monitoring; the strong thermal anisotropy limits the use of satellite thermal infrared (TIR) data. Hitherto, the poor investigation was focused on the modeling and analysis of urban thermal anisotropy (UTA), especially in mountainous urban areas with multi-slope environments. These areas exhibit a distinctive “roof effect”, which is defined as the radiative transfer effect between the roof and the adjacent wall due to the slope that results in different heights between the roofs; multiple scattering has also been changed. Although an analytical thermal anisotropy model for the urban canopy over sloping terrain (AU3SM) has been proposed, its inability to effectively account for roof effects and multi-scattering mechanisms limits its daytime TIR observation applicability. To address these limitations, we developed an enhanced AU3SM that considers the roof effect and multiple scattering, which is labeled AU3SM-RS. The model was evaluated using measurements based on unmanned aerial vehicles (UAVs) in the mountainous city of Chongqing, China, with values of the root mean square error (RMSE) and coefficient of determination (R2) of 0.83 K and 0.93 in UTA. Comparison with a graphic processing unit-based solution for the faster 3-D radiative transfer model (GRay) further validates the model’s reliability with RMSE and R2values of 0.12 K and 0.96, respectively. Simulations in a certain scenario reveal that as the slope increases, the roof effect increases and the multiple scattering effect decreases in UTA and brightness temperature (BT), ignoring the roof effect and multiple scattering can result in maximum UTA biases of approximately 0.54, 0.48, and 0.72 K, BT biases approximately 1.02, 1.62, and 2.4 K at 5°, 15°, and 30° slopes, the biases due to neglecting the second scattering are very slight compared to the first scattering. Under certain conditions with a slope of 10°, the wider roof and narrower roadway, a related more dramatic roof effect; the narrower and deeper street canyon, a related more dramatic scattering effect. The proposed model is an efficient computational tool to assess UTA in mountainous areas quickly.

Accurate and complete extraction of buildings from very high-resolution (VHR) remote sensing (RS) images is highly important for urban planning and land management. However, owing to the limited information available for small buildings and building boundaries, as well as challenges such as the spectral similarity of ground objects, tree occlusions, and shadow interference, automatically extracting buildings from VHR images remains challenging. These issues may result in building extraction errors such as misclassification, small building omissions, blurred boundaries, and incorrect segmentation. To address these challenges, we propose a multiscale hybrid transformer (MSHFormer) with boundary enhancement. This approach incorporates a hybrid encoder that combines a multiscale local perception (MSLP) module and a global perception module (GPM), combining the strengths of convolutional neural networks (CNNs) and transformers to achieve efficient synergy between global modeling and local feature extraction. In addition, we developed an edge enhancement module (EHM) to enhance boundary information, significantly improving building boundary segmentation accuracy. Finally, we design a group alignment feature fusion module (GAFFM) to efficiently integrate low-level features from the encoder with high-level features from the decoder, reducing feature space misalignment. The experimental results on three public datasets demonstrate the effectiveness of MSHFormer. Specifically, the proposed method achieves intersection-over-union (IoU) values of 89.1%, 73.6%, and 89.5% on the Potsdam, Massachusetts, and WHU datasets, respectively.

Traditional deep semantic segmentation methods rely heavily on large-scale, densely labeled data, which is costly and time-consuming to obtain. Semi-supervised frameworks have emerged as a promising alternative, reducing the dependency on pixel-level annotations while enhancing segmentation performance. However, these methods still face challenges in data augmentation and class imbalance. For instance, traditional augmentation techniques such as CutMix are limited by their reliance on single random local contexts, which restricts perturbation strength and hinders model generalization. Moreover, the long-tailed class distribution in remote sensing (RS) images is often overlooked, leading to pseudo-labels that are biased toward majority classes, further exacerbating class imbalance. To address these challenges,we propose a novel semi-supervised framework for RS semantic segmentation, named APRW, which incorporates adaptive patch augmentation and class rank weighting. The adaptive patch augmentation module dynamically generates adaptive patch masks by comparing the predictions of weakly and strongly augmented inputs. These masks focus on high-confidence regions while preserving diverse boundary structures, effectively expanding the perturbation space and enhancing the model’s adaptability to varied inputs. The class rank weighting module maintains memory banks for both labeled and unlabeled data, dynamically calculates class weights based on the relative ranking of pixel confidences, and adaptively fuses these weights. This design mitigates biases caused by long-tailed distributions, improves the segmentation of rare classes, and enhances overall segmentation accuracy. Extensive experiments demonstrate that our method consistently outperforms existing approaches across multiple RS datasets, including DFC22, iSAID, MER, MSL, Vaihingen, and GID-15, showcasing superior generalization and higher segmentation precision. Implementation is released at https://github.com/135az/APRW.

In this paper, we consider the need for deployment in the long-distance safe longitudinal formation control task when the connected autonomous vehicle (CAV) platoon is subjected to malicious cyber attacks. To ensure the safe, orderly, stable and efficient driving performance of the vehicle platoon, a novel distributed data-driven control (DDDC) approach for a homogeneous connected autonomous vehicle platoon under false data injection (FDI) attacks is investigated. First, an FDI attacks detection and compensation mechanism is designed to detect whether the received position signals are under attack or not and compensate the attacked position signals. Then, a novel DDDC approach for the vehicle platoon longitudinal formation control is developed by using the compensation data from the designed attack compensation mechanism and a dynamic linearization data model. Theoretical analysis verifies that the proposed DDDC method can ensure the internal stability (IS) and string stability (SS) of the homogeneous platoon subjected to FDI attacks. Finally, the effectiveness and practicality of the proposed DDDC approach are validated through a group of comparative simulations subjected to random FDI attacks of equal frequency and magnitude.Note to Practitioners—This work aims to solve the vehicle platoon long-distance safe longitudinal formation control task subjected to malicious FDI attacks. FDI attacks can achieve their destructive purposes by processing intercepted information and injecting false data into the original information. Existing literature overly relies on a priori knowledge of network attacks, yet in practice it is difficult to capture the true intentions of attackers in advance. For multi-channel V2V communication networks, it is even more important to design a resilient and accurate distributed controller strategy for such unpredictable and specific network attacks. Therefore, this paper proposes a data-driven distributed longitudinal formation control strategy with attack detection and compensation mechanism. The proposed strategy is shown to be able to ensure the safe longitudinal formation control task for the homogeneous CAV platoon suffering from FDI attacks. In addition, the stability of the CAV platoon is then investigated while the attacked signals are detected and cleaned, and it is shown to guarantee the internal stability of a single vehicle and the string stability of the platoon.

This paper investigates a distributed data-driven event-triggered fault-tolerant control for a connected heterogeneous vehicle platoon with sensor faults under the vehicle-to-vehicle (V2V) communication network. First, a sensor fault diagnosis scheme based on the high-gain observer is designed to detect, estimate and compensate for the fault signal. Then, the measurement signals with sensor faults are recovered, and the reconstructed system can be modeled by the full-form dynamic linearization (FFDL) technique. To obtain reliable vehicle data communication with the efficient use of network resources, an event-triggered mechanism based on the formation error is established, and then a distributed data-driven controller can be designed to accomplish the platoon formation control task. Theoretical analysis demonstrates that the proposed distributed event-triggered fault-tolerant control method can realize the task of ensuring the safe formation control of the platoon system under some sensor faults. Finally, a simulation of a platoon with three faulty vehicles is made to verify the effectiveness and real-time performance of the proposed method.

In this article, an improved model-free adaptive control (iMFAC) is proposed for discrete-time multi-input multioutput (MIMO) nonlinear systems with an event-triggered transmission scheme and quantization (ETQ). First, an event-triggered scheme is designed, and the structure of the uniform quantizer with an encoding-decoding mechanism is given. With the concept of partial form dynamic linearization based on event-triggered and quantization (PFDL-ETQ), a linearized data model of the MIMO nonlinear system is constructed. Then, an improved model-free adaptive controller with the ETQ process is designed. By this design, the update of the pseudo partitioned Jacobean matrix (PPJM) estimates and control inputs occurs only when the trigger conditions are met, which reduces the network transmission burden and saves the computing resources. Theoretical analysis shows that the proposed iMFAC with the ETQ process can achieve a bounded convergence of tracking error. Finally, a numerical simulation and a biaxial gantry motor contour tracking control system simulation are given to illustrate the feasibility of the proposed iMFAC method with the ETQ process.

Multilabel remote sensing (RS) image annotation is a challenging and time-consuming task that requires a considerable amount of expert knowledge. Most existing RS image annotation methods are based on handcrafted features and require multistage processes that are not sufficiently efficient and effective. An RS image can be assigned with a single label at the scene level to depict the overall understanding of the scene and with multiple labels at the object level to represent the major components. The multiple labels can be used as supervised information for annotation, whereas the single label can be used as additional information to exploit the scene-level similarity relationships. By exploiting the dual-level semantic concepts, we propose an end-to-end deep learning framework for object-level multilabel annotation of RS images. The proposed framework consists of a shared convolutional neural network for discriminative feature learning, a classification branch for multilabel annotation and an embedding branch for preserving the scene-level similarity relationships. In the classification branch, an attention mechanism is introduced to generate attention-aware features, and skip-layer connections are incorporated to combine information from multiple layers. The philosophy of the embedding branch is that images with the same scene-level semantic concepts should have similar visual representations. The proposed method adopts the binary cross-entropy loss for classification and the triplet loss for image embedding learning. The evaluations on three multilabel RS image data sets demonstrate the effectiveness and superiority of the proposed method in comparison with the state-of-the-art methods.

The performance of hyperspectral image (HSI) classification relies on the pixel information obtained from hundreds of contiguous and narrow spectral bands. Existing approaches, however, are limited to exploit an appropriate latent subspace for data representation within the pixel-level or superpixel-level. To utilize spectral information and spatial correlation among pixels in HSI and avoid the “salt-and-pepper” problem generated in the pixel-based HSI classification, a novel pixel-level and superpixel-level aware subspace learning method called PSASL is developed. The PSASL constructs the subspace learning framework based on the reconstruction independent component analysis algorithm. The spectral–spatial graph regularization and label space regularization are developed as the pixel-level constraints. To avoid the “salt-and-pepper” problem generated in the pixel-based classification methods, superpixel-level constraints are introduced for integrating the data representations defined in the subspace and class probabilities of the pixels in the same superpixel. The subspace learning and the pixel-level regularization are combined with the superpixel-level regularization to form a unified objective function. The solution to the objective function is efficiently achieved by employing a customized iterative algorithm, and it converges very fast. A discriminative data representation and a universal multiclass classifier are learned simultaneously. We test the PSASL on three widely used HSI data sets. Experimental results demonstrate the superior performance of our method over many recently proposed methods in HSI classification.

The challenges in hyperspectral image (HSI) classification lie in the existence of noisy spectral information and lack of contextual information among pixels. Considering the three different levels in HSIs, i.e., subpixel, pixel, and superpixel, offer complementary information, we develop a novel HSI feature learning network (HSINet) to learn consistent features by self-supervision for HSI classification. HSINet contains a three-layer deep neural network and a multifeature convolutional neural network. It automatically extracts the features such as spatial, spectral, color, and boundary as well as context information. To boost the performance of self-supervised feature learning with the likelihood maximization, the conditional random field (CRF) framework is embedded into HSINet. The potential terms of unary, pairwise, and higher order in CRF are constructed by the corresponding subpixel, pixel, and superpixel. Furthermore, the feedback information derived from these terms are also fused into the different-level feature learning process, which makes the HSINet-CRF be a trainable end-to-end deep learning model with the back-propagation algorithm. Comprehensive evaluations are performed on three widely used HSI data sets and our method outperforms the state-of-the-art methods.

Low-rank representation (LRR) can construct the relationships among pixels for hyperspectral image (HSI) classification with a given dictionary and a noise term. However, the accuracy of HSI classification based on LRR methods is degraded with the redundant and noise information existed in pixels. The neglect of semantic information around pixels in the LRR methods may cause “salt-and-pepper” problem in HSI classification. To avoid the aforementioned problems, a novel self-supervised low-rank representation method called SSLRR is developed. In SSLRR, the LRR and spectral–spatial graph regularization are developed as the pixel-level constraints to remove the redundant and noise information in HSIs. Superpixel constraints including data structure and relationship construction are further utilized to provide supervised feedback information to the subspace learning to avoid the “salt-and-pepper” problem generated in the pixel-based classification methods, and simultaneously enhance the performance of LRR. The pixel-level and superpixel-level regularizations are explicitly integrated into a unified objective function for LRR. By means of the linearized alternating direction method with adaptive penalty, the solution to the objective function is achieved by employing a customized iterative algorithm. We perform comprehensive evaluation of the proposed method on three challenging public HSI data sets. We obtain new state-of-the-art performance on these data sets, and achieve improvements of 44.3%, 13.4%, and 30.1% in overall accuracy compared to the best LRR method.