Band selection (BS) is a commonly used dimension reduction technique for hyperspectral images. In this article, we propose a similarity-based ranking (SR) strategy inspired by a density-based clustering algorithm. The representativeness of a band is evaluated according to its ability to become a cluster center. We introduce structural similarity (SSIM) to measure the relationships between the bands. Thus, our proposed ranking-based BS method is called SR-SSIM. We picked state-of-the-art BS methods as competitors and carried out classification experiments on different data sets. The results illustrated that SR-SSIM outperformed the other methods. It is demonstrated, in this article, that the SSIM is more suitable for hyperspectral BS than the Euclidean distance since the SSIM can mine the spatial information contained in the band images. Furthermore, we discuss the application of BS methods on deep learning classifier. We found that proper preprocessing by the BS method can effectively eliminate redundant information and avoid overfitting.

Magnetic dipole localization methods have a wide application range. Resolving equations of magnetic gradient tensor is a classical method to obtain the direction of the relative position vector, but the real solution hides in four possible solutions. The Scalar Triangulation and Ranging (STAR) method is also effective but is less accurate than the real solution of tensor equations. To solve the two problems, we proposed a novel direction determination approach by combining the advantages of the two methods. Based on the cube tensor measurement array from the STAR method, a novel way to calculate the distance between the dipole and the sensor array is proposed. Then, a novel real-time magnetic dipole localization method is obtained. Results of simulation experiments indicate that our method raises the successful localization area by 43% compared with the traditional method, and the baseline length of the sensor array dominates the performance of our method.

Geomagnetic vector field is always measured by a strapdown triaxial magnetometer on a vehicle. The magnetometer measurements are polluted by the magnetometer inherent errors, misalignment error, and vehicle-generated magnetic inferences. With the aid of absolute attitude information from the gyro, the calibration accuracy has been proved to be greatly improved, especially for vector measurements. However, the performance of calibration methods is greatly affected by the gyro bias, which causes error accumulated over time in the absolute attitude information. In this work, we propose an improved calibration method robust to gyro bias based on relative attitude information from the gyro. Simulation results indicated that the proposed method can estimate the calibration parameters more accurately and stably than the traditional method when the gyro bias exists. The influence on the calibration accuracy of the different parts of the gyro bias is also analyzed by simulation. Experiment using a low-cost gyro proved that the proposed method can effectively improve the estimation accuracy of the calibration parameters, as the influence on the traditional method of the gyro bias is 2-3 orders of magnitude higher than that on the proposed method.

SUMMARY: The reliable classification of seismic events is crucial to precise seismic cataloging and robust hazard evaluation. Recent advances in deep learning have achieved great success in seismic event identification, leveraging their exceptional ability to automatically extract and recognize features. However, existing deep learning approaches to seismic classification rely exclusively on deterministic models, which cannot quantify epistemic uncertainty, preventing the estimation of prediction confidence that is critical for reliability evaluation. To address this issue, in this paper, we develop two uncertainty-aware deep learning models for earthquake (EQ) versus explosion (EP) classification using the DiTing 2.0 artificial intelligence training data set: a Bayesian convolutional neural network (BCNN) and a dropout-based CNN (DropCNN). We also implement a conventional deterministic CNN as a baseline model for comparative analysis. The experimental results demonstrate that both the BCNN and DropCNN can achieve a classification accuracy comparable to the conventional CNN, while providing additional uncertainty metrics for estimation confidence of prediction. Crucially, their uncertainty scores increase markedly in terms of encountering misclassifications or out-of-distribution samples compared to correct classifications, enabling automatic rejection of unreliable predictions based on the uncertainty threshold setting, triggering human verification or alternative discrimination methods. We then apply the trained models to analyse suspicious explosion events in the DiTing 2.0 data set. The BCNN and DropCNN results exhibits strong agreement, consistently identifying 79 EP and two EQ events and flagging the remaining samples as uncertain classifications needing further verification. Our findings demonstrate that deep learning methods incorporating uncertainty estimation not only maintain a high accuracy in seismic event discrimination but also provide uncertainty estimation. This capability significantly enhances the model’s reliability and decision-making value in practical applications.