Effective feature extraction is a key issue in hyperspectral image (HSI) classification task. Recent works have studied hyperspectral classification models based on various deep architectures. However, the specific architecture cannot fully exploit the complementary diversity of global and local features in HSIs, resulting in suboptimal results. To address these issues, we fully utilize the advantages of GNN and CNN in global and local feature extraction and design a new end-to-end global–local feature fusion enhancement network (GLFFEN). Specifically, we first construct a GNN with dynamically weighted neighbor contributions using superpixel-segmented patches as nodes, named the Graph Attention (GA) branch. Additionally, we design a spatial–spectral feature attention module (SSFAM) to enhance the ability of the CNN to extract spatial and spectral features in local neighborhoods, termed the spatial–spectral feature attention (SSFA) branch. Moreover, a multi-feature adaptive fusion (MAF) module is proposed to solve the problem of weight distribution during global–local feature fusion. Experiments on three well-known HSI datasets have shown that our GLFFEN surpasses state-of-the-art (SOTA) methods on three widely used metrics.

Hyperspectral anomaly detection (HAD) remains a critical challenge in remote sensing, aiming to precisely separate sparse, unknown anomalies from complex, high-proportion backgrounds. Although deep learning architectures, particularly the Transformer, dominate HAD, their effectiveness is constrained by two fundamental deficiencies: the architectural flaw of “uniform processing” across feature tokens and the microscopic reliance on fixed non-linear activation functions, which are mathematically insufficient for modeling the complex HSI spectral features. To address this dual challenge, this paper introduces the Synergistic Kolmogorov–Arnold Network and Fidelity-Gated Transformer (KANGT) framework. This novel framework integrates two synergistic innovations: the Fidelity-Gated Context-Aware Transformer (GCAT), which employs a reconstruction fidelity-based gating module named the Contextual Feature Matching Module (CFMM) to explicitly and dynamically separate background and anomaly processing streams, and the KAN-MLP module, which replaces traditional Feed-Forward Networks (FFNs) with learnable, spline-based functions, enabling superior adaptive non-linear feature approximation. Extensive experiments on challenging real-world HSI datasets consistently demonstrate KANGT’s superior performance compared to existing methods, and the average AUC reached 0.9921 on eight datasets. This work establishes a robust new paradigm for HAD, with future efforts aimed at optimizing the computational efficiency of KANs to meet real-time application demands.

Despite the substantial body of work that has achieved large-scale data expansion using anchor-based strategies, these methods incur linear complexity relative to the sample size during iterative processes, making them quite time-consuming. Moreover, as feature dimensionality reduction is often overlooked in this procedure, most of them suffer from the “curse of dimensionality”. To address all these issues simultaneously, we introduce a novel paradigm with a superpixel encoding and data projecting strategy, which learns a small-scale bi-stochastic graph from the data matrix with large-scale pixels and high-dimensional spectral features to achieve effective clustering. Moreover, a symmetric neighbor search strategy is integrated into our framework to ensure the sparsity of graph and further improve the calculation efficiency. For optimization, a simple yet effective strategy is designed, which simultaneously satisfies all bi-stochastic constraints while ensuring convergence to the optimal solution. To validate our model’s effectiveness and scalability, we conduct extensive experiments on various-scale hyperspectral images (HSIs). The results demonstrate that our method achieves the state-of-the-art clustering performance, and can be better extended to large-scale and high-dimensional HSIs.