Deep Learning (DL) has become a popular method for Remote Sensing (RS) Change Detection (CD) due to its superior performance compared to traditional methods. However, generating extensive labeled datasets for DL models is time-consuming and labor-intensive. Additionally, the imbalance between changed and unchanged areas in object CD datasets, such as buildings, poses a critical issue affecting DL model efficacy. To address this issue, this paper proposes a change detection enhancement method using artificial image generation and attention mechanism. Firstly, the content of the imbalanced CD dataset is enhanced using a data augmentation strategy that synthesizes effective building CD samples using artificial RS image generation and building label creation. The created building labels, which serve as new change maps, are fed into a generator model based on a conditional Generative Adversarial Network (c-GAN) to generate high-resolution RS images featuring building changes. The generated images with their corresponding change maps are then added to the CD dataset to create the balance between changed and unchanged samples. Secondly, a channel attention mechanism is added to the proposed Adversarial Change Detection Network (Adv-CDNet) to boost its performance when training on the imbalanced dataset. The study evaluates the Adv-CDNet using WHU-CD and LEVIR-CD datasets, with WHU-CD exhibiting a higher degree of sample imbalance compared to LEVIR-CD. Training the Adv-CDNet on the augmented dataset results in a significant 16.5% F1-Score improvement for the highly imbalanced WHU-CD. Moreover, comparative analysis showcases the superior performance of the Adv-CDNet when complemented with the attention module, achieving a 6.85% F1-Score enhancement.

Accurate and reliable speed and position estimation plays an important role in the safety and efficiency of intelligent railway vehicles. Due to the level required of safety, reliability, and strong norms in the current practical application, intelligent railway vehicle positioning heavily relies on a large number of balises laid on the track and the onboard odometer (OD), while the other position method, GNSS introduction, is relatively slow. This article proposed a digital track map-assisted onboard railway location system using strapdown inertial navigation system (SINS) and OD. The proposed method consists of two steps. First, an SINS- and OD-integrated navigation method based on OD velocity integration is in the inner circle. Then, a map-matching algorithm based on vertical projection and heading weighting was employed, and when the matching outer circle results were obtained, the positions obtained from the matching outer circles were used to replace the positions obtained from SINS/OD for the Kalman filter combination. The performance of our algorithm was verified using field tests, and SINS/OD and SINS/OD/MM comparison data processing results prove that our proposed digital track map-assisted SINS/OD algorithm can effectively suppress the accumulation of train position errors. After nearly 80 km of navigation, the position error is 24 m, and the relative mileage accuracy is less than or equal to 0.03% distance.

Various systems have been developed to process agricultural land data for better management of crop production. One such system is Cropland Data Layer (CDL), produced by the National Agricultural Statistics Service of the United States Department of Agriculture (USDA). The CDL has been widely used for training deep learning (DL) segmentation models. However, it contains various errors, such as salt-and-pepper noise, and must be refined before being used in DL training. In this study, we used two approaches to refine the CDL for DL segmentation of major crops from a time series of Sentinel-2 monthly composite images. Firstly, different confidence intervals of the confidence layer were used to refine the CDL. Secondly, several image filters were employed to improve data quality. The refined CDLs were then used as the ground-truth in DL segmentation training and evaluation. The results demonstrate that the CDL with +45% and +55% confidence intervals produced the best results, improving the accuracy of DL segmentation by approximately 1% compared to non-refined data. Additionally, filtering the CDL using the majority and expand–shrink filters yielded the best performance, enhancing the evaluation metrics by about 1.5%. The findings suggest that pre-filtering the CDL and selecting an effective confidence interval can significantly improve DL segmentation performance, contributing to more accurate and reliable agricultural monitoring.

The demodulation phase error will cause the quadrature error to be coupled to the rate output, resulting in performance deterioration of the MEMS gyroscope. To solve this problem, an in-run automatic demodulation phase error compensation method is proposed in this paper. This method applies square wave angular rate input to the gyroscope and automatically identifies the value of the demodulation phase error through the designed automatic identification algorithm. To realize in-run automatic compensation, the demodulation phase error corresponding to the temperature point is measured every 10 °C in the full-temperature environment (−40~60 °C). The relationship between temperature and demodulation phase error is fitted by a third-order polynomial. The temperature is obtained by the temperature sensor and encapsulated in the ceramic packages of the MEMS gyroscope, and the in-run automatic compensation is realized based on the fitting curve. The temperature hysteresis effect on the zero-rate output (ZRO) of the gyroscope is eliminated after compensation. The bias instability (BI) of the three gyroscopes at room temperature (25 °C) is reduced by four to eight times to 0.1°/h, while that at full-temperature environment (−40~60 °C) is reduced by three to four times to 0.1°/h after in-run compensation.

The increasing demand for food and rapid population growth have made advanced crop monitoring essential for sustainable agriculture. Deep learning models leveraging multispectral satellite imagery, like Sentinel-2, provide valuable solutions. However, transferring these models to diverse regions is challenging due to phenological differences in crop growth stages between training and target areas. This study proposes the Adaptive Month Matching (AMM) method to align the phenological stages of crops between training and target areas for enhanced transfer learning in cropland segmentation. In the AMM method, an optimal Sentinel-2 monthly time series is identified in the training area based on deep learning model performance for major crops common to both areas. A month-matching process then selects the optimal Sentinel-2 time series for the target area by aligning the phenological stages between the training and target areas. In this study, the training area covered part of the Mississippi River Delta, while the target areas included diverse regions across the US and Canada. The evaluation focused on major crops, including corn, soybeans, rice, and double-cropped winter wheat/soybeans. The trained deep learning model was transferred to the target areas, and accuracy metrics were compared across different time series chosen by various phenological alignment methods. The AMM method consistently demonstrated strong performance, particularly in transferring to rice-growing regions, achieving an overall accuracy of 98%. It often matched or exceeded other phenological matching techniques in corn segmentation, with an average overall accuracy across all target areas exceeding 79% for cropland segmentation.

Detecting binary changes in co-registered bitemporal hyperspectral images (HSIs) using deep learning methods is challenging due to the high dimensionality of spectral data and significant variations between images. To address this challenge, previous approaches often used dimensionality reduction methods separately from the change detection network, leading to less accurate results. In this study, we propose an end-to-end fully connected adversarial network (EFC-AdvNet) for binary change detection, which efficiently reduces the dimensionality of bitemporal HSIs and simultaneously detects changes between them. This is accomplished by extracting critical spectral features at the pixel level through a self-spectral reconstruction (SSR) module working in conjunction with an adversarial change detection (Adv-CD) module to effectively delineate changes between bitemporal HSIs. The SSR module employs a fully connected autoencoder for hyperspectral dimensionality reduction and spectral feature extraction. By integrating the encoder segment of the SSR module with the change detection network of the Adv-CD module, we create a generator that directly produces highly accurate change maps. This joint learning approach enhances both feature extraction and change detection capabilities. The proposed network is trained using a comprehensive loss function derived from the concurrent learning of the SSR and Adv-CD modules, establishing EFC-AdvNet as a robust end-to-end network for hyperspectral binary change detection. Experimental evaluations of EFC-AdvNet on three public hyperspectral datasets demonstrate that joint learning between the SSR and Adv-CD modules improves the overall accuracy (OA) by 5.44%, 10.43%, and 7.52% for the Farmland, Hermiston, and River datasets, respectively, compared with the separate learning approach.