Remote sensing-based landslide segmentation is of great significance for geological hazard assessment and post-disaster rescue. Existing convolutional neural network methods, constrained by the inherent limitations of spatial convolution, tend to lose high-frequency edge details during deep semantic extraction, while frequency-domain analysis, although capable of globally preserving high-frequency components, struggles to perceive local multi-scale features. The lack of an effective synergistic mechanism between them makes it difficult for networks to balance regional integrity and boundary precision. To address these issues, this paper proposes the Geometric Context and Frequency Domain Fusion Network (GCF-Net), which achieves explicit edge enhancement through a three-stage progressive framework. First, the Pyramid Lightweight Fusion (PGF) block is proposed to aggregate multi-scale context and provide rich hierarchical features for subsequent stages. Second, the Geometric Context and Frequency Domain Fusion (GCF) module is designed, where the frequency-domain branch generates dynamic high-frequency masks via the Fourier transform to locate boundary positions, while the spatial branch models foreground–background relationships to understand boundary semantics, with both branches fused through an adaptive gating mechanism. Finally, Edge-aware Detail Consistency Improvement (EDCI) module is designed to balance boundary preservation and noise suppression based on edge confidence, achieving adaptive output refinement. Under the joint supervision of Focal loss, Dice loss, and Edge loss, experiments on the mixed dataset and LMHLD dataset demonstrate that GCF-Net achieves OAs of 96.42% and 96.71%, respectively. Ablation experiments and visualization results further validate the effectiveness of each module and the significant improvement in boundary segmentation.

Transformer models have been widely adopted for hyperspectral image (HSI) classification due to their exceptional long-sequence modeling capabilities. However, the self-attention mechanism in Transformers incurs quadratic computational complexity, posing challenges in both speed and memory consumption. Recently, a novel state-space model (SSM)—the Mamba model—has emerged, overcoming the quadratic complexity of self-attention by achieving linear computational complexity while retaining powerful long-sequence modeling. Yet, the original Mamba design does not account for the unique spectral–spatial characteristics of HSI data, making it difficult to capture multiscale features. This limitation can lead to the loss of critical spectral–spatial cues at fine targets and complex boundaries, resulting in increased classification noise, blurred boundary segmentation, and reduced overall accuracy. To address the loss of fine‑grained spectral–spatial information in HSI, we propose HyM3S: a Hyperspectral Multi‑scale Spatial–Spectral Sequence model that integrates multiscale spatial–spectral convolutions with Mamba's linear sequence modeling.HyM3S first extracts multiscale spatial and spectral features in parallel along horizontal and vertical branches, and reinforces salient channels via channel‑wise attention. Features are then adaptively fused across modality and directional dimensions to form a unified joint representation. Finally, this representation is fed into the Mamba module for long‑range dependency modeling under linear complexity, thereby significantly improving classification accuracy and suppressing noise. Experiments on four benchmark HSI datasets—Pavia University (PaviaU), Houston2013, WHU‑Hi‑HanChuan, and WHU‑Hi‑HongHu—demonstrate the clear superiority of the proposed HyM3S model for hyperspectral image classification.

Vehicle platoons require low-latency, reliable V2V/V2I links, yet authentication, edge processing and active attacks consume scarce resources and introduce delay and uncertainty; prior work mostly treats control, communication and security separately. In this letter, we close this gap with a risk-aware joint control–communication–security framework and a practical solver, the Distributionally Robust Primal–Dual Alternating (DR–PDA) algorithm, which couples LQR-based control updates with SCA-based convex surrogates and Wasserstein–CVaR robustness for integer resource allocation. Numerical results show DR–PDA achieves low total cost, reduced leakage and attack success, modest per-epoch runtime and a small optimality gap; ablations confirm the Wasserstein–CVaR correction is critical for tail safety, while removing modeled authentication delay lowers cost but markedly increases leakage, illustrating the latency–security trade-off.