This paper proposes a fair control framework for multi-robot systems, which integrates the newly introduced Alternative Authority Control (AAC) and Flexible Control Barrier Function (F-CBF). Control authority refers to a single robot which can plan its trajectory while considering others as moving obstacles, meaning the other robots do not have authority to plan their own paths. The AAC method dynamically distributes the control authority, enabling fair and coordinated movement across the system. This approach significantly improves computational efficiency, scalability, and robustness in complex environments. The proposed F-CBF extends traditional CBFs by incorporating obstacle shape, velocity, and orientation. FCBF enhances safety by accurate dynamic obstacle avoidance. The framework is validated through simulations in multi-robot scenarios, demonstrating its safety, robustness and computational efficiency.

In recent advancements, hyperspectral image (HSI) classification through few-shot learning (FSL) has significantly progressed. Domain adaptation, integrated with FSL, effectively utilizes transferable knowledge from a source domain (SD) with abundant labeled data to excel in classification tasks within a target domain (TD) with scarce labels. However, most existing methods usually use traditional convolutional neural networks (CNNs) to extract local spatial information to characterize and mine feature and distribution information while ignoring the underlying topological relationships among feature classes. Therefore, we propose a topology graph perception cross-domain FSL (TGP-CFSL) framework that leverages graph information aggregation. Specifically, to construct the extended topological relationships of the target, we have designed a topological graph-based multiscale fusion (TGMF) feature extraction module, which is adept at fully mining the topological spatial neighborhood information of the target. Meanwhile, a dual-graph information perception (DGIP) module is designed, which is able to characterize and aggregate intradomain topological relationships in terms of both feature representations and interdomain distribution similarities and to extract higher order domain distribution information for realizing domain alignment. Experimental results on three public HSI datasets demonstrate that the proposed method outperforms existing methods.

The contraflow left-turn (CLT) lane is a successful intersection design that can effectively increase the throughput of the left-turn traffic flow by allowing left-turn vehicles to use the exit lane. However, the noncooperative longitudinal and lane choice behavior of left-turn vehicles may underutilize the CLT lane. Further, left-turn traffic flow throughput changes with respect to the traffic situation and it will not achieve the maximum even if the left-turn traffic flow is split evenly among the lanes. To address these issues, we propose a cooperative longitudinal driving and lane assignment strategy that seeks to control the lane choices and longitudinal driving behavior of connected and autonomous vehicles (CAVs) at CLT intersections to minimize the total left-turn traffic delay. This approach establishes a mixed-integer linear model to optimize the longitudinal driving and lane choices for all left-turn vehicles simultaneously. It also takes into account the physical constraints of vehicles and the operational rules of the CLT intersection to ensure traffic safety and efficiency. To apply it in real time, a computationally efficient solution algorithm is developed based on the Lagrange relaxation method. It can solve the mixed-integer optimization problem within 0.02 seconds for vehicles less than 10. Numerical results demonstrate that the proposed cooperative control method outperforms the noncooperative-based control strategies developed upon the intelligent driver model (IDM) with a 24.66% reduction in average delay and a 20.44% increase in throughput. The proposed control strategy can enhance the left-turn traffic safety and efficiency for intersections with a CLT lane.

Supervised classification of hyperspectral image (HSI) is generally required to obtain better performance in spectral-spatial feature learning by fully using complex pixel- and superpixel-level interdependencies with small labeled samples. Limited by the local regular convolutions, convolutional neural networks (CNNs) can only exploit information from the short-range Euclidean neighbors of a target, hindering the effectiveness of feature representation. In contrast, graph convolutional networks (GCNs) can learn long-range dependencies between non-Euclidean neighbors but usually require the input of a full graph constructed from a whole HSI, making GCNs must be trained in a full-batch manner with tremendous computational consumption. In this work, we propose a composite neighbor-aware convolutional metric network (CNCMN), aiming to learn each target's representation from its composite neighbors (i.e., both Euclidean and non-Euclidean neighbors) in a batchwise manner. Specifically, for each target in an HSI, its Euclidean neighbors are the pixels in the local square region centered on itself, and its non-Euclidean neighbors are several related nodes selected from the constructed full graph. Correspondingly, a composite convolution (CoConv) is proposed by coupling an image convolution and a graph convolution, which can perform flexible convolutions on those composite neighbors and extract adaptively fused features from them. Besides, to further boost classification, we also propose a mini-batch metric classifier to dynamically optimize interclass and intraclass distances of samples batch by batch, which is then combined with the CoConv to form the mini-batch CNCMN. Extensive experiments on three real-world HSIs demonstrate the advantages of the proposed method over mini-batch deep learning algorithms and have obtained the state-of-the-art performance in these fields. The code is available at: https://github.com/qichaoliu/HSI-CNCMN.

Multilabel image classification aims to assign images to multiple possible labels. In this task, each image may be associated with multiple labels, making it more challenging than the single-label classification problems. For instance, convolutional neural networks (CNNs) have not met the performance requirement in utilizing statistical dependencies between labels in this study. Additionally, data imbalance is a common problem in machine learning that needs to be considered for multilabel medical image classification. Furthermore, the concatenation of a CNN and a transformer suffers from the disadvantage of lacking direct interaction and information exchange between the two models. To address these issues, we propose a novel hybrid deep learning model called CTransCNN. This model comprises three main components in both the CNN and transformer branches: a multilabel multihead attention enhanced feature module (MMAEF), a multibranch residual module (MBR), and an information interaction module (IIM). The MMAEF enables the exploration of implicit correlations between labels, the MBR facilitates model optimization, and the IIM enhances feature transmission and increases nonlinearity between the two branches to help accomplish the multilabel medical image classification task. We evaluated our approach using publicly available datasets, namely the ChestX-ray11 and NIH ChestX-ray14, along with our self-constructed traditional Chinese medicine tongue dataset (TCMTD). Extensive multilabel image classification experiments were conducted comparing our approach with excellent methods. The experimental results demonstrate that the framework we have developed exhibits strong competitiveness compared to previous research. Its robust generalization ability makes it applicable to other medical multilabel image classification tasks.

Heterogeneous bi-temporal images have different visual appearances and inconsistent data distribution for the same scene, making it challenging to detect changes, which need to align the shared information and reduce various unwanted sensor-related noises for comparability. Mainstream methods usually adopt two types of techniques: feature transformation and image translation. The former relies on handcrafted priors while the latter lacks constraints on unwanted backgrounds, leading to limitations such as a lack of robustness to non-intrinsic changes (e.g., seasonal and atmospheric changes, and sensor-related noise) and unsatisfactory detection performance. To overcome these drawbacks, we propose a novel unsupervised predict-refining-involved bidirectional contrastive difference network (PRBCD-Net) composed of a coarse prediction module and iterative refining modules. Each refining module utilizes feature extractors with a cross-reconstruction constraint and bidirectional contrastive constraint to extract discriminative features, and then generate a refined change map by change map optimizers. Two advantages of the proposed PRBCD-Net are: 1) the cross-reconstruction constraint is used to promote the feature distribution consistency of the bi-temporal images by using the forward and backward transformations; 2) the bidirectional contrastive constraint is used to improve the discriminability of features by narrowing the gap between non-intrinsic changes while widening intrinsic changes under the guidance of a coarse change map. Thus, the refining module can generate a finer change map than the coarse one, and the performance can be further improved through multiple iterations. Experimental results demonstrate the effectiveness and robustness of the proposed method compared with state-of-the-art methods.

Subspace clustering (SC) has achieved remarkable success in hyperspectral images (HSIs) due to the powerful representation ability of handling high-dimensional complex data. However, most of the existing SC methods focus on linear subspace representation and ignore the more effective nonlinear representation. Besides, SC suffers from the bottlenecks, such as high computation load and memory capacity, due to the spectral decomposition of adjacency matrix for large HSIs. To overcome these limitations, we propose an end-to-end learnable network framework for large HSIs, called self-supervised deep multi-level subspace clustering (S2DMSC), which incorporates the convolutional neural network (CNN) module, multi-level subspace clustering (MSC) module, and high-quality pseudo-label-based self-supervised learning module into a unified learning framework. More concretely, the deep multi-level spatial-spectral representation from hierarchical superpixels is modeled as a sparsity-constrained self-expression module for SC to construct high-quality pseudo-labels to learn the network parameters and produce better clusters for hyperspectral pixels. Experimental results on four classical HSIs demonstrate the effectiveness of S2DMSC and exhibit superior clustering performance compared to the representative clustering methods.

Hyperspectral image (HSI) classification faces the inherent challenge of small sample learning, primarily due to the difficulty in labeling vast land covers. Meta-learning, with its ability to learn transferable meta-knowledge from existing HSIs, is seen as a promising solution. However, different HSIs usually have varying distributions manifested as differing spectral wavelengths and reflectance shifts, which is often neglected in existing methods, stalling the acquisition of transferable features. To address this issue, we introduce an attribute-driven spectral alignment (ADSA) method, which parameterizes and embeds spectral attributes (i.e., spectral wavelengths and reflectance shifts) into a domain-adaptation model, aiming to decouple the domain-specific attributes of different HSIs in an unsupervised manner. After training, the parameterized attributes of source-domain (SD) HSIs can be substituted with those of the target domain (TD), allowing the decoder of ADSA to rebuild new HSIs sharing identical spectral attributes. By this means, numerous distribution-consistent labeled samples preserving inherent spectral–spatial structures can be obtained. Then, a 3-D residual prototypical network (RPN) based on 3-D convolutions and metric learning is designed to model complex structures of HSIs, which in combination with the few-shot learning (FSL) framework can extract valuable discriminative knowledge from these auxiliary samples. Finally, by applying this learned knowledge to the TD HSI, only a small number of labeled samples are required to obtain satisfactory performance. Extensive experiments on four real-world HSIs demonstrate the effectiveness of our method, and the performance outperforms several state-of-the-art methods.

Limited by the shape-fixed kernels, convolutional neural networks (CNNs) are usually difficult to model difform land covers in hyperspectral images (HSIs), leading to inadequate land use. Recently, benefiting from the ability to conduct shape-adaptive convolutions and model complex patterns in graph-structured data, graph convolutional networks (GCNs) have been applied to HSI classification. However, due to the massive computation in GCNs, HSI is usually pretreated into a graph based on a specific superpixel segmentation, which limits the modeling of spatial topologies to the same scale. To break this limitation, we propose a multilevel superpixel structured graph U-Net (MSSGU) to learn multiscale features on multilevel graphs. Specifically, we construct several hierarchical segmentations from fine to coarse by progressively merging adjacent superpixels and then convert them into multilevel graphs. Meanwhile, based on the merging relations between hierarchical superpixels, we establish the pooling and unpooling functions to transfer features from one graph to another, thereby enabling different-level graphs to collaborate in a single network. Different from concatenating different-scale features straightforwardly in the feature fusion stage, MSSGU fuses them in a coarse-to-fine progressive manner, which can generate subtler fusion features adaptive to the pixelwise classification task. Moreover, we use a CNN instead of GCN to extract and fuse the pixel-level features, which greatly reduces the computation. Such a hybrid U-Net can exploit features of HSIs from a multiscale hierarchical perspective, and its performance has been proven competitive with other deep-learning-based methods by extensive experiments on three benchmark datasets.

Recently, the graph convolutional network (GCN) has drawn increasing attention in the hyperspectral image (HSI) classification. Compared with the convolutional neural network (CNN) with fixed square kernels, GCN can explicitly utilize the correlation between adjacent land covers and conduct flexible convolution on arbitrarily irregular image regions; hence, the HSI spatial contextual structure can be better modeled. However, to reduce the computational complexity and promote the semantic structure learning of land covers, GCN usually works on superpixel-based nodes rather than pixel-based nodes; thus, the pixel-level spectral–spatial features cannot be captured. To fully leverage the advantages of the CNN and GCN, we propose a heterogeneous deep network called CNN-enhanced GCN (CEGCN), in which CNN and GCN branches perform feature learning on small-scale regular regions and large-scale irregular regions, and generate complementary spectral–spatial features at pixel and superpixel levels, respectively. To alleviate the structural incompatibility of the data representation between the Euclidean data-oriented CNN and non-Euclidean data-oriented GCN, we propose the graph encoder and decoder to propagate features between image pixels and graph nodes, thus enabling the CNN and GCN to collaborate in a single network. In contrast to other GCN-based methods that encode HSI into a graph during preprocessing, we integrate the graph encoding process into the network and learn edge weights from training data, which can promote the node feature learning and make the graph more adaptive to HSI content. Extensive experiments on three data sets demonstrate that the proposed CEGCN is both qualitatively and quantitatively competitive compared with other state-of-the-art methods.

We present a novel auto-machine learning partial differential equations (PDE) driven deep learning framework for the classification of hyperspectral images (HSIs). The work is inspired by the famous PDE in image processing, namely, the Perona-Malik (PM) equation, which can form a scale-space and is capable of edge-preserving denoising using anisotropic diffusion (PM diffusion). In this framework, we firstly propose auto-machine learning-based trainable PM diffusion blocks (TPM-blocks) and then cascade them into a deep convolutional neural networks (CNN). Specifically, the 1 ×1 convolution layer and the trainable PM diffusion unit (TPMDU) are integrated as the TPM-block, and then multiple TPM-blocks are stacked to form a novel end-to-end deep learning architecture. We show that our deep learning method has the capacity of learning discriminative spectral and spatial features of HSIs. Experimental results on several popular datasets demonstrate that the proposed method achieves state-of-the-art performance compared with the several existing deep learning-based methods.

Convolutional neural networks (CNNs) have emerged as a powerful tool in remote sensing image analysis. However, the layer-by-layer convolutions (L2Convolutions) in CNNs cannot fully exploit the relativities of pixels in the 3D cube data, especially for hyperspectral images (HSIs). In this paper, a directional message propagation convolutional neural network framework (MPCNN), is proposed for the supervised classification of HSIs. In the proposed framework, we integrate a novel multi-directional message propagation mechanism, namely slice-by-slice convolutions (S2Convolutions), into the hidden feature layers which are generated by L2Convolutions to propagate feature information between feature maps in the same layer. Owing to the S2Convolutions, abundant and discriminative spectral-spatial feature learning can be enhanced compared with traditional CNNs without S2Convolutions. The performance of the proposed MPCNN is evaluated on benchmark dataset of HSIs, and quantitative and qualitative experiments show that the performance of the proposed method outperforms several state-of-the-art methods.

Convolutional neural networks (CNNs) are of great interest and have demonstrated remarkable performance in hyperspectral images (HSIs) classification. However, due to the current configuration of the convolution layers with a fixed kernel shape, regular CNNs are inherently limited in modeling the diverse land-cover structures, particularly in the cross-classes edge regions, where irregular class boundaries would lead to high classification errors. To address this issue, we propose a content-guided CNN (CGCNN) for HSI classification. Compared with the shape-fixed kernel in the traditional CNN, the proposed content-guided convolution adaptively adjusts its kernel shape according to the spatial distribution of land covers. The content pattern is reflected by a latent guide map automatically learned from HSI. Such content-adaptive kernel with CGCNN could suppress the irregularity and unexpected features in class boundaries and, thus, improve the feature learning in cross-classes regions. Based on the content-guided convolution, a novel guided feature extraction unit (GFEU) is constructed for spectral-spatial feature learning of HSI. Finally, the CGCNN classification framework is established by stacking multiple GFEUs with dense connection, which is helpful for mitigating the gradient vanishing and increasing the robustness to overfitting. Extensive experiments on several HSIs demonstrate that the proposed approach possesses great details' preserving ability and its performance outperforms other state-of-the-art methods.

Sparse subspace clustering (SSC) algorithm has achieved an impressive performances in hyperspectral images clustering. However, the raw samples contained noises were used to construct the dictionary. Moreover, SSC represented each signal individually ignoring the relationship among hyperspectral pixels. To overcome these problems, we propose a sparse dictionary learning method for hyperspectral images clustering, in which joint sparsity and shared Wavelets are integrated to improve the expressive power of the learnt dictionary. First, we incorporate the shared Wavelets as a base dictionary into a unified joint sparsity constrained optimizing model to learn a structured sparse dictionary from both spectral and contextual characteristics of hyperspectral images. Then, the sparse representation coefficients based on the learnt sparse dictionary are adopted to construct a non-negative affinity matrix of graph. Finally, spectral clustering is employed to the affinity matrix to obtain the final clustering result. Experimental results clearly demonstrate that the proposed algorithm outperforms other state-of-the-art methods on the hyperspectral dataset.

Limited and expensive availability of labeled training samples resulted in the development of methods defining the hyperspectral classification task in the form of data augmentation based supervised learning. However, most of the methods just implicitly utilize the spectral-spatial information in the isotropic neighborhood, instead of explicitly indicating the anisotropic or steering neighborhood system. In this paper, we apply steering stencils for estimating the local directional homogenous regions and exploiting more valuable spectral-spatial contexts. By using a best steering stencil matching method, we propose a data augmentation and refining method to improve the performance of any spectral-spatial classifier with limited labeled samples. Experiments show that the proposed method is very effective for many spectral-spatial classifiers.

With the rapid development of model-driven engineering (MDE), domain-specific modeling has become a widely used software development technique. In MDE, metamodels represent a schema definition of the syntax and static semantics to which an instance model conforms (i.e., a model conforms to its metamodel in a similar manner to how a program conforms to a grammar). However, in order to address new feature requests of the domain and language, the metamodel often undergoes frequent evolution that may result in the inability of users to load and view previous model instances. MARS is a metamodel recovery system to address the problems of metamodel evolution. This paper presents our extensions to MARS to infer models for multi-tiered domains. A new XSLT translator has been developed to generate a domain-specific language (DSL) called MRL (model representation language) for the XML representation of domain instances. The metamodel inference engine has been revised to translate the MRL back into a metamodel.

In model-driven engineering, metamodels may get lost over time resulting in the inability to load and view existing model instances. MARS is a system that recovers metamodels from model instances using grammar inference. This paper discusses advances in MARS that improve accuracy and scalability.