Xiyan Sun

Hyperspectral images (HSIs) are inevitably degraded by mixed noise, which hampers downstream interpretation. Recent graph-signal-processing denoisers encode the spatial–spectral structure of an HSI through a product graph over superpixel bodies, yet the adopted Kronecker (tensor) product graph retains only the joint spatial–spectral edges and discards the pure-spatial and pure-spectral edges—the two priors that govern HSI smoothness. We introduce a two-parameter weighted product-graph family that contains the Kronecker, Cartesian and strong products as exact special cases, and propose Weighted Strong Product Graph Laplacian Regularization (WSPGLR)—the strong-product branch with a tunable joint-edge weight β—embedded in a global low-rank plus sparse model solved by the Alternating Direction Method of Multipliers (ADMM) with singular-value-thresholding and soft-thresholding updates and a sparse conjugate-gradient (CG) solve. On three simulated cubes (Washington DC Mall, Pavia University, Indian Pines) under four mixed-noise scenarios, WSPGLR consistently improves Mean Peak Signal-to-Noise Ratio (MPSNR) and ERGAS (Erreur Relative Globale Adimensionnelle de Synthèse) over a matched Kronecker-product control in every tested case (and Mean Structural Similarity (MSSIM) in 11 of 12 settings)—up to 2.5 dB in MPSNR from the graph term alone—and an ablation shows that β governs a spatial–spectral fidelity trade-off (Spectral Angle Mapper, SAM). On the detailed urban scenes, WSPGLR attains the highest MPSNR against the external methods and matched control in seven of eight settings, whereas it trails LRTDTV on the smooth agricultural scene; with an optional scene-adaptive TV step it attains the best average rank across scene types; a Tucker-based variant further shows the low-rank block is modular and generally improves spectral fidelity. Tests on four no-reference real HSIs and 30 paired real-noise MEHSI samples extend the sensor coverage; on MEHSI, the native-domain RND framework remains substantially stronger, which delimits the scope of the proposed training-free regularizer.

In GNSS carrier-phase observations, rapid and accurate integer ambiguity resolution is essential for centimeter-level positioning. With BeiDou-3 and Galileo operational, rising dimensionality challenges LAMBDA with exponential complexity and heavy dependence on float-solution quality. This paper proposes a Capuchin-Cuckoo Cooperative Search Algorithm (CCCSA), combining capuchin global exploration with cuckoo Levy flights to escape local optima. In simulated integer least-squares experiments, CCCSA reduces resolution time by 67% and 10% versus LAMBDA and M-LAMBDA, maintaining over 98% success in 12-dimensional scenarios; in a 42.7 km long-baseline test, its success rate reaches 77.4%, surpassing LAMBDA by 25 percentage points. To overcome the traditional Ratio test’s deficiency—a one-dimensional scalar ratio without spatial topological awareness—this paper proposes an Enhanced Sharpness-Adaptive Ratio test (ESA-Ratio) with multi-dimensional feature fusion. Full-threshold scanning shows ESA-Ratio operates safely at threshold 2.0, whereas the traditional Ratio requires 3.0 to mitigate false-peak risks, with a true fix rate of 97.55% and a false fix rate of 0.14%. CDF analysis shows a 95% confidence three-dimensional positioning error of 0.0166 m versus 0.0170 m for the traditional ratio. These results confirm that, in the tested static single-system scenarios, ESA-Ratio improves availability, integrity, and positioning accuracy simultaneously at a lower threshold.

Rainfall-induced landslides result from interactions between terrain predisposition and hydrometeorological forcing. In event-scale susceptibility modelling, uncertainty often arises from non-landslide controls. Locations without recorded failures may differ in rainfall history, storm exposure, or inventory completeness, which can bias models trained on static absence samples. This study develops a hydrometeorological control sampling strategy for rainfall induced landslide susceptibility modelling. The strategy defines each sample by grid cell and rainfall date, and constructs non landslide controls from storm related risk sets. A background predisposition prior is used to screen candidate controls. Regional same date controls, annular hard controls near failed slopes, and cross year rainy season background controls are then integrated to represent complementary hydrometeorological and terrain conditions. Design weights and density ratio calibration are applied to account for control reliability and reduce distribution mismatch between the training sample and the mapping domain. In the sample-level evaluation, the method was evaluated in Pubei County, Guangxi, China, using terrain, geology, land cover, daily and antecedent rainfall, and surface wetness. It outperformed Buffer, LowSlope, and IV Low across five classifiers. Relative to IV Low, mean AUC increased from 0.887 to 0.958, accuracy from 81.8% to 91.6%, and Kappa from 63.6% to 83.3%. Holdout validation of two July 2006 landslide clusters also showed greater concentration in top-ranked areas. Averaged over RF and GBDT, top 10% capture rose from 0.227 to 0.322, while the frequency ratio increased from 2.264 to 3.213. These findings suggest that, under the evaluated conditions, the strategy improves sample discrimination, increases landslide concentration in areas ranked as highly susceptible, and reduces uncertainty in the selection of nonlandslide controls.

Low-latitude ionospheric irregularities cause rapid fluctuations in GNSS signal amplitude and phase, threatening receiver tracking and services. We propose a dual-channel XGBoost–Transformer–LSTM fusion model to address rare events, missed-detection costs, and the difficulty of combining physics-informed and profile-structure information. Data from the HKOH station and geospace products spanning 1 January 2020–21 March 2024 were used. Space-weather variables, temporal factors, previous-day scintillation status, and a TEC120 latitude–UT grid at 06:00–11:00 UT were used to predict GPS L1 amplitude scintillation during 11:00–14:00 UT. The XGBoost channel used the raw TEC grid, first-order latitudinal and hourly TEC differences, and background variables, whereas the Transformer–LSTM channel extracted within-day structure. Channel probabilities were combined by soft voting, with the weight and threshold selected on the validation set. Chronological test results were recall = 0.8788, F1-score = 0.5949, ROC-AUC = 0.8263, PR-AUC = 0.6097, CSI = 0.4234, TSS = 0.4511, and MCC = 0.4096. Fusion raised recall from 0.8030 to 0.8788 but reduced precision from 0.4609 to 0.4496. Paired bootstrap analysis supported a stable improvement only in recall. The model therefore suits warning applications prioritizing detection over minimizing false alarms.

Reliable landslide susceptibility mapping (LSM) depends not only on classifier selection but also on the construction of non-landslide samples. Conventional random or buffer-based sampling can retain candidate negatives that are environmentally similar to landslides, increasing label ambiguity and reducing model reliability. This study proposes an IF-KMeans negative sampling framework to refine candidate non-landslide samples for LSM in Wuzhou City, China. Isolation Forest was trained using 395 mapped landslides and then applied to 2000 candidate negative samples to remove samples with high similarity to the landslide feature space; K-Means clustering was subsequently used to stratify the retained candidates and select representative negative samples. The optimized samples were evaluated using six classifiers, including LR, SVM, MLP, RF, XGBoost, and LightGBM, and compared with conventional buffer-based sampling. The IF-KMeans framework consistently improved AUC across the six classifiers, with gains of 0.041–0.081, and the IF-KMeans-RF model achieved the highest AUC of 0.944. Additional diagnostics showed that the IF-removed samples were closer to known landslides in environmental feature space and were located in areas with higher local landslide density, indicating higher potential confusion risk. These findings suggest that positive-sample-guided negative-sample refinement can reduce ambiguity in LSM training data and improve the reliability of susceptibility mapping for geological disaster prevention and risk mitigation.