Remote sensing (RS) image scene classification has wide applications in the field of RS. Although existing methods have achieved remarkable performance, there are still limitations in feature extraction and lightweight design. Current multi-branch models, although performing well, have large parameter counts and high computational costs, making them difficult to deploy on resource-constrained edge devices such as unmanned aerial vehicles (UAVs). On the other hand, lightweight models like StarNet, having less parameter, but rely on element-wise multiplication to generate features and lack the capture of explicit long-range spatial feature, resulting in insufficient classification accuracy. To address these issues, this letter proposes a lightweight mamba-based hybrid network, namely LMHMamba, whose core is an innovative lightweight multi-feature hybrid mamba (LMHM) module. This module combines the advantage of StarNet in implicitly generating high-dimensional nonlinear features, introduces a lightweight state space module to enhance spatial feature learning capabilities, and then uses local and global attention modules to emphasize local and global features. This enables effective multi-dimensional feature fusion while maintaining low parameter. We validate the performance of LMHMamba model on three remote sensing scene classification datasets and compare it with mainstream lightweight models and the latest methods. Experimental results show that LMHMamba achieves advanced levels in both classification accuracy and computational efficiency, significantly outperforming existing lightweight models, providing an efficient solution for edge deployment. Code is available at https://github.com/yizhilanmaodhh/LMHMamba.

Remote sensing image change detection (CD), as a pivotal technology for monitoring Earth’s surface dynamics, plays a crucial role in urban planning, resource management, and disaster assessment. Despite the success of deep learning-based methods, they still suffer from two significant limitations. Firstly, the inadequate exploitation of frequency-domain information restricts their ability to capture subtle structural and edge changes. Secondly, the suboptimal interaction strategies that predominantly rely on attention mechanisms or direct feature exchange that fails to fully model complex semantic differences between bi-temporal images. To address these challenges, we propose a Wavelet Attention-based Group Interaction Network (WAGINet), which leverages wavelet attention for joint frequency-spatial domain feature learning and uses a group-wise feature exchange mechanism to optimize bi-temporal interaction. The wavelet attention module decomposes features into high-low frequency components and emphasize important ones to improve edge-aware feature extraction. In the meanwhile, the group interaction strategy enables both channel-group and spatial-group feature exchange to capture the correlation between bi-temporal features while better protecting structural integrity, so that promotes more discriminative change representation. Experimental results on public LEVIR-CD and WHU-CD datasets show that WAGINet outperforms existing state-of-the-art methods, providing an effective solution for high-precision remote sensing image CD in complex scenarios. Code available at https://github.com/yizhilanmaodhh/WAGINet.

As one of the most promising classification approaches , association rule-based classification (also called associative classification , AC) enables effectively integrating the classification tasks with association rule discovery techniques for deriving accurate, robust and interpretable results. The advantages of association rule discovery techniques over deep learning architectures in classification are mainly reflected by the aspects of better interpretability for users and higher accuracy for small sample data. Despite of great progress in both theoretical and applied aspects, there remains a lack of comprehensive and systematic overview for the recent development in AC. In light of this, this paper first conducts a statistical analysis of academic reports and related application achievements over the past decades, among which 317 are method-oriented and 200 are application-oriented. After that, through performing an in-depth analysis for these literatures, it then provides a comprehensive review for the overall learning framework, theoretical methodologies, application domains, as well as the key research challenges in the field of AC. Finally, this review displays some potential and meaningful research directions in the future by integrating the challenges with development trends, such as deep associative learning framework, human–machine intelligent associative system and semi-supervised learning within evidential framework, with the hope of assisting interested researchers gaining a quick understanding for the development trends in AC.

Change detection from synthetic aperture radar (SAR) imagery is critical in remote sensing research. Existing methods have made significant progress in the application of convolutional neural networks (CNNs) and attention mechanisms. However, traditional CNNs suffer the limitations in feature representation due to their depth and width constraints, and struggle to effectively capture complex interactions between image features. To address these issues, we propose a novel dynamic bilinear fusion network (DBFNet) for change detection in SAR imagery. First, to compensate for the lack of traditional convolutional representation capability, we design a dynamic shift convolution module that adaptively aggregates multiple convolution kernels and shifts pixels, enabling richer and more detailed features to be extracted. Second, a bilinear fusion module (BFM) is designed to generate the bilinear joint representation between parallel features by computing a matrix outer product of feature maps. The parallel features include both intraimage and interimage features, thereby effectively modeling the complex interactions and capturing the dependence relationship between spatiotemporal features. The experimental results on three real SAR datasets demonstrate the superior performance of DBFNet compared to existing state-of-the-art methods. The codes are available athttps://github.com/yizhilanmaodhh/DBFNet.

The problem of the large-scale interconnected system (LSIS) control is prevalent in practical engineering and is becoming increasingly complex. In this article, we propose a novel decentralized event-triggered tracking control (ETTC) strategy for a class of continuous-time nonlinear LSIS with unmatched interconnected terms and asymmetric input constraints. First, auxiliary subsystems are established to address the unmatched cross-linking terms. Next, the dynamics states of the tracking error and the exosystem are combined to construct a nominal augmented subsystem. By employing a nonquadratic performance function, the input-constrained decentralized tracking control problem is transformed into an optimal control problem for the nominal augmented subsystem. A group of independent parameters and event-triggered conditions are designed to save communication bandwidth and computational resources. Subsequently, the critic-only adaptive dynamic programming (ADP) method is used to solve the Hamilton-Jacobi-Bellman equation (HJBE) associated with the optimal control problem. To improve training success rate, the weights of the critic neural network (NN) are updated by introducing a particle swarm optimization algorithm (PSOA). The tracking error and the NN weights are proved to be uniformly ultimately bounded (UUB) under the proposed ETTC by using the Lyapunov extension theorem. Finally, the simulation example of an unmatched interconnected system is provided to verify the validity of the proposed decentralized method.

Nowadays, remote sensing change detection (CD) plays an important role in Earth observation applications. Recently, the value of cross-modal CD has gradually emerged because of the complementary features in content. However, existing domain adaption-based methods are generally limited to the optical domain and suffer from imbalanced information between modalities. In this paper, a novel CD method based on dual image translation and dual contrastive learning (C3D) is proposed for heterogeneous remote sensing images, including a translation module and a CD module. First, the translation module aims to learn a comparable representation between the different domains through a dual contrastive learning structure based on feature samples, which can break the consistency constraint and better solve the imbalanced information. Then the similarity metric of patches is compared by contextual features at different scales in the CD module to achieve a more accurate classification of changed and unchanged pixels. The C3D is compared with state-of-the-art methods and validated by the basic experimental results on five datasets. In addition, further experiments on the translation module were also performed to explore the effectiveness of contrastive learning in the CD task.

Imputing the incomplete patterns in clustering tasks is a common but risky procedure, because the estimated values may affect the real distribution of the data and deteriorate the results. To address this problem, a new belief-based incomplete pattern unsupervised classification method (BPC) with uncertainty and imprecision reasoning is proposed in this paper. First, the complete patterns are grouped into a few clusters to obtain the corresponding reliable centers, and thereby are divided into reliable patterns and unreliable ones by an optimization method. Second, a basic classifier trained by reliable patterns is employed to classify unreliable patterns and incomplete patterns edited by the neighbors. Finally, some imprecise patterns are carefully reassigned again by a new distance-based rule depending on the obtained reliable centers and belief functions theory. The simulation results show that the BPC has the potential to deal with real datasets.

The clustering of incomplete patterns is a very challenging task because the estimations may negatively affect the distribution of real centers and thus cause uncertainty and imprecision in the results. To address this problem, a new belief-based incomplete pattern unsupervised classification method (BPC) is proposed in this paper. First, the complete patterns are grouped into a few clusters by a classical soft method like fuzzy$c$-means to obtain the corresponding reliable centers and thereby are partitioned into reliable patterns and unreliable ones by an optimization method. Second, a basic classifier trained by reliable patterns is employed to classifies unreliable patterns and the incomplete patterns edited by the neighbors. In this way, most of the edited incomplete patterns can be submitted to specific clusters. Finally, some ambiguous patterns will be carefully repartitioned again by a new distance-based rule depending on the obtained reliable centers and belief functions theory. By doing this, a few patterns that are very difficult to classify between different specific clusters will be reasonably submitted to meta-cluster which can characterize the uncertainty and imprecision of the clusters due to missing values. The simulation results show that the BPC has the potential to deal with real datasets.