This paper presents a methodology for predicting remaining train arrival times along a predefined railway path using sectionally sampled GPS-based observations collected between consecutive stations. Station-level remaining arrival times are derived from multiple train trips and analyzed using a linear regression model, enabling assessment of both prediction accuracy and temporal consistency. The approach provides continuous predictions of remaining arrival times at each station along the path rather than focusing solely on the final destination. Results demonstrate that median remaining arrival time errors decrease with increasing sampling density, an effect attributed to greater sample availability that captures a larger proportion of the underlying train dynamics and timetable adherence. Under these conditions, the proposed methodology achieves a station-based average median remaining arrival time error of less than 91 seconds. Across 100 repeated out-of-sample evaluations, the results indicate reproducible performance and acceptable coarseness even when the sampling density is substantially reduced. This continuous, station-level prediction capability enables early detection of delays and potential timetable conflicts, providing minute-level predictive accuracy suitable for decision support for train dispatchers.

This paper presents a change detection algorithm based on Shannon entropy and the Rayleigh distribution for both single-look and multi-look SAR images. While hypothesis testing has been widely used in SAR change detection, few studies have applied this approach to both types of data. To address this, we propose a method that utilizes Shannon entropy to detect changes between two samples. The performance of the algorithm was evaluated through Monte Carlo simulations using synthetic SAR data and further validated on real-world datasets, including single-look images from the CARABAS II dataset and multi-look data from the UAVSAR radar. The results demonstrate that the proposed method is effective in detecting changes. This paper highlights the versatility of the approach, which is capable of handling both single-look and multi-look SAR data, and reinforces the way for future research into alternative entropy measures and probability distributions in change detection tasks.

The combination of a large number of students and a diverse student population poses additional pedagogical challenges in higher education courses. The teacher's perception of student engagement becomes more challenging in a large group, and students may experience reduced motivation despite the teacher's efforts and pedagogical approach. This paper discusses the challenges of teaching approaches and students' motivation in large groups. For that, a survey at Blekinge Tekniska Hogskola (BTH) in Sweden was performed to evaluate and discuss learning improvement in science, technology, engineering, and mathematical (STEM) courses attended by Swedish and international students. The survey explores how teachers can encourage student motivation in large classes, and it was based on related works reporting teaching methods and common issues in teaching STEM courses. Based on the survey's result analysis, we can observe that the physical learning environment and teaching style do not play an essential role in the student's motivation. However, the teacher and student interaction influences their motivation. These findings provide insight into the diverse perceptions of students regarding the connections between teaching style, feedback, learning environment, and motivation, for example. The conclusions derived from the survey are expected to serve as guidelines for improving student performance in large STEM groups.

This letter introduces a novel regression model structure for the inflated Rayleigh distribution, which effectively models high dynamic amplitude pixel values in synthetic aperture radar (SAR) images. The proposed model estimates the mean of inflated Rayleigh distribution signals by a structure that includes a set of regressors and a link function. The inflated Rayleigh distribution combines the Rayleigh and a degenerate distribution, assigning nonnull probability specifically for observed values equal to zero. Null pixel values in amplitude SAR images can be randomly distributed within the image, especially in low-intensity areas; a model capable of incorporating these values is essential to avoid changes in image statistics. Extensive evaluations are conducted using simulated and real SAR images to validate the proposed model, specifically focusing on ground-type detection in high dynamic amplitude pixel values scenarios. The performance of the proposed inflated Rayleigh regression model is compared with traditional Gaussian-based regression models, excelling in terms of ground-type detection in an SAR image obtained from the ICEYE radar.

In this paper, we present three algorithms for the multitemporal synthetic aperture radar (SAR) images coregistration. The proposed algorithms are a 2-D cross correlation, a 1-D parabolic based, and a 2-D projective transformation. The 2-D cross correlation algorithm is used to obtain coarse estimation of the displacement for coregistration. In the second method, two independent 1-D parabolic interpolations are calculated to refine the estimation of the peak location of the cross correlation matrix with subpixel accuracy. Finally, in the third method, a 2-D projective transformation is employed to align the SAR images using point correspondences and the cubic interpolation. The performance evaluation of these algorithms are provided based on the coherence magnitude and the absolute displacement error for a point target using a corner reflector in the scene. The experimental results obtained on real recorded multitemporal satellite SAR data demonstrate the effectiveness and the computational complexity of these algorithms.

Synthetic aperture radars (SAR) data plays an important role in remote sensing applications. It is common knowledge that SAR image amplitude pixels can be approximately modeled by the Rayleigh distribution. However, this model is contin-uous and does not accommodate points with non-zero prob-ability, such as a null pixel amplitude value. Thus, in this paper, we propose an inflated Rayleigh distribution for SAR image modeling that is based on a mixed continuous-discrete distribution and can be used to fit signals with observed values on$[0,\ \infty)$. The maximum likelihood approach is considered to estimate the parameters of the proposed distribution. An empirical experiment with a SAR image is also presented and discussed.

This letter presents two new change detection (CD) methods for synthetic aperture radar (SAR) image stacks based on the Neyman–Pearson criterion. The first proposed method uses the data from wavelength–resolution images stack to obtain background statistics, which are used in a hypothesis test to detect changes in a surveillance image. The second method considersa prioriinformation about the targets to obtain the target statistics, which are used together with the previously obtained background statistics, to perform a hypothesis test to detect changes in a surveillance image. A straightforward processing scheme is presented to test the proposed CD methods. To assess the performance of both proposed methods, we considered the coherent all radio band sensing (CARABAS)-II SAR images. In particular, to obtain the temporal background statistics required by the derived methods, we used stacks with six images. The experimental results show that the proposed techniques provide a competitive performance in terms of probability of detection and false alarm rate compared with other CD methods.

The Rayleigh regression model was recently proposed for modeling amplitude values of synthetic aperture radar (SAR) image pixels. However, inferences from such model are based on the maximum-likelihood estimators, which can be biased for small-signal lengths. The Rayleigh regression model for SAR images often considers small pixel windows, which may lead to inaccurate results. In this letter, we introduce bias-adjusted estimators tailored for the Rayleigh regression model based on: 1) Cox and Snell’s method; 2) Firth’s scheme; and 3) the parametric bootstrap method. We present numerical experiments considering synthetic and actual SAR data sets. The bias-adjusted estimators yield nearly unbiased estimates and accurate modeling results.

The presence of outliers (anomalous values) in synthetic aperture radar (SAR) data and the misspecification in statistical image models may result in inaccurate inferences. To avoid such issues, the Rayleigh regression model based on a robust estimation process is proposed as a more realistic approach to model this type of data. This article aims at obtaining Rayleigh regression model parameter estimators robust to the presence of outliers. The proposed approach considered the weighted maximum likelihood method and was submitted to numerical experiments using simulated and measured SAR images. Monte Carlo simulations were employed for the numerical assessment of the proposed robust estimator performance in finite signal lengths, their sensitivity to outliers, and the breakdown point. For instance, the nonrobust estimators show a relative bias value 65-fold larger than the results provided by the robust approach in corrupted signals. In terms of sensitivity analysis and break down point, the robust scheme resulted in a reduction of about 96% and 10%, respectively, in the mean absolute value of both measures, in compassion to the nonrobust estimators. Moreover, two SAR datasets were used to compare the ground type and anomaly detection results of the proposed robust scheme with competing methods in the literature.

This article presents two supervised change detection algorithms (CDA) based on convolutional neural networks (CNN) that use stacks of co-registered wavelength-resolution synthetic aperture radar (SAR) images to detect changes in an image under monitoring. The additional information of a scene of interest provided by SAR image stacks can be explored to enhance the performance of change detection algorithms. In particular, stacks of images with similar statistics can be obtained for ultra-wideband (UWB) very high frequency (VHF) SAR systems, as they produce images highly stable in time. The proposed CDAs can be summed up into four stages: difference image formation, semantic segmentation, clustering, and change classification. The CNN-GSP algorithm is based on a ground scene prediction (GSP) image, which is used as a reference to form a difference image (DI). A CNN-based model then analyzes the DI. The CNN-MDI algorithm feeds multiple DIs with identical monitored images to a CNN-based model, which will concurrently analyze their features. Tests with CARABAS-II data show that the proposed CDAs can outperform other state-of-the-art algorithms that also use stacks of WR-SAR images. Beyond that, the proposed algorithms outperformed a CNN-based CDA that does not use image stacks, which shows that CNN-based algorithms can use the additional information provided by stacks of SAR images to reduce false alarm occurrences while increasing the probability of detection of changes.

This letter presents a clutter statistical analysis for stacks of wavelength-resolution synthetic aperture radar (SAR) images. Each image stack consists of SAR images generated by the same sensor, using the same flight track illuminating the same scene but with a time separation between the illuminations. We test three candidate statistical distributions for time changes in the stack, namely, Rician, Rayleigh, and log-normal. The tests results reveal that the Rician distribution is a very good candidate for modeling stack of wavelength-resolution SAR images, where 98.59% of the tested samples passed the Anderson-Darling (AD) goodness-of-fit test. Also, it is observed that the presence of changes in the ground scene is related to the tested samples that have failed in the AD test for the Rician distribution hypothesis.

This letter proposes a regression model for nonnegative signals. The proposed regression estimates the mean of Rayleigh distributed signals by a structure which includes a set of regressors and a link function. For the proposed model, we present: 1) parameter estimation; 2) large data record results; and 3) a detection technique. In this letter, we present closed-form expressions for the score vector and Fisher information matrix. The proposed model is submitted to extensive Monte Carlo simulations and to the measured data. The Monte Carlo simulations are used to evaluate the performance of maximum likelihood estimators. Also, an application is performed comparing the detection results of the proposed model with Gaussian-, Gamma-, and Weibull-based regression models in synthetic aperture radar (SAR) images.