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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. [ABSTRACT FROM AUTHOR]

Ships are important targets for modern naval warfare detection and reconnaissance. The accurate detection of ships contributes to the maintenance of maritime rights and interests and the realisation of naval strategy. Synthetic Aperture Radar (SAR) image detection tasks play a vital role in ship detection, which has consistently been a research hotspot in the field of SAR processing. Although significant progress has been achieved in SAR ship detection techniques using deep learning methods, some challenges still persist. Natural images and SAR images significantly diverge in imaging mechanisms and scattering characteristics. In complex background environments, ships exhibit multiscale variations and dense arrangements, and numerous small-sized ships may be present, culminating in false or missed detections. To address these issues, we propose a novel SAR ship detection network, namely, a Wavelet-Driven Feature-Enhanced Attention–You Only Look Once X (WDFA-YOLOX) network. Firstly, we propose a Wavelet Cascade Residual (WCR) module based on the traditional image processing technique wavelet transform, which is embedded within an improved Spatial Pyramid Pooling (SPP) module, culminating in the formation of the effective wavelet transform-based SPP module (WSPP). The WSPP compensates for the loss of fine-grained feature information during pooling, enhancing the capability of the network to detect ships amidst complex background interference. Secondly, a Global and Local Feature Attention Enhancement (GLFAE) module is proposed, leveraging a parallel structure that combines convolutional modules with transformer modules to reduce the effect of irrelevant information and effectively strengthens valid features associated with small-sized ships, resulting in a reduction in false negatives in small-sized ship detection. Finally, a novel loss function, the Chebyshev distance-generalised IoU loss function, is proposed to significantly enhance both the precision of the detection box and the network convergence speed. To support our approach, we performed thorough experiments on the SSDD and HRSID, achieving an average precision (AP) of 99.11% and 96.20%, respectively, in ship detection. The experimental results demonstrate that WDFA-YOLOX has significant advantages in terms of detection accuracy, generalisation capability, and detection speed and can effectively realise more accurate detection in SAR images, consistently exhibiting superior performance and application value in SAR ship detection. [ABSTRACT FROM AUTHOR]

Low Earth orbit (LEO) satellite constellations have emerged as an effective alternative for the provision of high-accuracy positioning, navigation and timing (PNT) solutions which are based on high-precision orbit and clock information. Determining an orbit with high precision is dependent on the number and distribution of ground tracking stations. Therefore, it is important to investigate methodologies that can ensure the adequate observing coverage of LEO navigation constellations. In this study, an evolutionary algorithm is applied to optimize the number and deployment of ground stations for tracking LEO constellations. According to the distribution area, two schemes of study are analyzed: (a) global deployment—the ground stations are deployed throughout the globe; (b) regional deployment—a selected region is used for deployment. For global deployment, the optimization objectives are focused on the ground station and observing rate for k-heavy observing coverage (HC), while the sole objective for the regional deployment scheme is the satellite position dilution of precision (SPDOP). It is shown that a deployment of 95 ground stations is optimal for achieving 3-HC with an observing rate of 97.37% and 4-HC with an observing rate of 92.01%. For regional distribution, 15, 20 and 25 ground stations are used for three optimal configurations of SPDOP at 2.058, 1.399 and 1.330, respectively. The results are significantly enhanced using intersatellite links for SPDOP evaluation, from 2.058, 1.399 and 1.330 to 0.439, 0.422 and 0.409, with 15, 20 and 25 ground stations, respectively. [ABSTRACT FROM AUTHOR]

Multilabel classification in remote sensing is very significant and plays an important role in extracting valuable information from satellite imagery. Ignoring the distinct information provided by labels in each image or transforming images into content-aware category representations without considering the inherent correlation of labels within the dataset can result in the establishment of improper relationships between images and labels, ultimately leading to a significant degradation in accuracy. To address this problem, this article proposes a label-driven graph convolutional network (LD-GCN) to excavate substantial information using the inherent correlation of labels from datasets and build a strong relationship between labels and images. The framework consists of two modules, i.e., the label recognition GCN (LRGCN) and the semantic enrichment module (SEM). The LRGCN module yields rich and valuable information from the inherent correlation of labels and builds a strong relationship between images and labels. The SEM further enriches the semantics obtained from LRGCN. Experiments conducted on UCM, AID, and DFC15 multilabel remote sensing datasets illustrate that LD-GCN outperforms the state-of-the-art methods on key evaluation metrics. [ABSTRACT FROM AUTHOR]

To reduce the influence of the static unbalance on an infrared missile guidance system, a new static unbalance measure system for the gimbals axes has been developed. Considering the coupling effects caused by a mass eccentricity, the static balance condition and measure sequence for each gimbal axis are obtained. A novel static unbalance test approach is proposed after analyzing the dynamic model of the measured gimbal axis. This approach is to drive the measured gimbal axis to do sinusoidal reciprocating motion in a small angle and collect its drive currents in real time. Then the static unbalance of the measured gimbal axis can be obtained by the current multi-cycle integration. Also a measuring system using the proposed approach has been developed. A balanced simulator is used to verify the proposed approach by the load and repeatability tests. The results show the proposed approach enhances the efficiency of the static unbalance measurement, and the developed measuring system is able to achieve a high precision with a greater stability.

The current Global Positioning System (GPS) provides limited availability and capability for a country like Japan where mountainous terrain and urban canyons do not allow a clear skyline to the horizon. At present, the Japanese Quasi-Zenith Satellite System (QZSS) is under investigation through a cooperative effort between the government and the private sector. QZSS is considered a multi-function satellite system, as it is able to provide communication, broadcasting, and positioning services for mobile users in a specified region with a high elevation angle. The additional GPS compatible signals from QZSS can remarkably improve the availability, accuracy, and capability of GPS positioning. This work focuses on the performance of GPS augmentation using the proposed QZSS. The QZSS satellite constellation and signal structure are briefly reviewed. Positioning with pseudo-range and carrier phase are discussed. The performance of GPS augmentation using QZSS in the Asian-Pacific and Australian area is studied using software simulations. The results are presented using the number of visible satellites as a measure of availability, GDOP as a measure of accuracy, and ambiguity success rate as a measure of capability of carrier-phase-based positioning with spatial and temporal variations. The results show that the QZSS will improve not only the availability and accuracy of GPS positioning, but will also enhance the capability of the GPS carrier-phase-based positioning in Japan and neighboring regions.

Summary: Communication through satellite is of great task and importance especially in the world of telecommunication infrastructure. It plays a leading role for future communication systems. However, the performance of the satellite communication system is dominated by the channel conditions overwhelmed by multipath fading and ionospheric scintillation effects. Maximum ratio combining (MRC) previously used to tackle this problem is characterized with hardware complexity resulted in long processing time, while threshold combining (TC) with simple hardware display poor performance. Thus, evaluation of hybrid TC‐MRC, with closed‐form expression over composite Rayleigh and Rician fading channel is necessary. In this paper, TC and MRC are combined to improve the signal quality and performance. The data are modulated using M‐ary quadrature amplitude modulation and transmitted over the combined effects. The received signals at varying paths are scanned to select the best paths. A mathematical expression using the probability density function of composite Rayleigh and Rician fading channel for mean integrated square error (MISE) is derived. The model is efficiently simulated and the performance is estimated. The results show that the hybrid TC‐MRC model achieves lower MISE and processing time compared to threshold combiner and even maximum ratio combiner, and thereby enhance the performance of the satellite communication system. [ABSTRACT FROM AUTHOR]

Understanding, analysis, monitoring and modelling of urban expansions are most important for urban planners in the process of sustainable development. This article investigated spatio-temporal process of urban expansion using cellular automata (CA) model integrated with artificial neural network (ANN) method. This integrated model was calibrated with data from 1974 to 2018, and used to predict expansions for 2030 and 2040 with two datasets of explanatory variables including slope, forest, river, specially protected areas, road, railroad and urban centres in an urban and non-urban area. The urban suitable area was estimated by criteria evaluation weights and analytic hierarchy process (AHP). The results show that the urban growth of Ulaanbaatar was equal to +36,201 ha (84.8%) for the period of 1974–2018 and this city will be projected to reach approximately +43,111 ha by 2030 (+50.3% from 2018) and +111,441 ha by 2040 (+44.3% from 2030 and totally +72.3% from 2018). [ABSTRACT FROM AUTHOR]