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Ionospheric scintillation is one of the main error sources of Global Navigation Satellite System (GNSS) positioning. The presence of scintillation may result in cycle slips, measurement errors or even losses of lock on satellites, eventually leading to complete failure of positioning. Typically, scintillation parameters S4 and σϕ are used to characterize amplitude and phase scintillation, respectively. However, the scintillation parameters can only be generated from data with a frequency of at least 1 Hz. Rate of change of total electron content index (ROTI) is often used as a proxy for scintillation parameters, which can be obtained from 1/30 Hz data. However, previous research has shown the inefficiency of ROTI to represent scintillation. Therefore, the multipath parameter (MP) has been proposed as another proxy for scintillation parameters, which can also be obtained from 1/30 Hz data. In this paper, both MP and ROTI (standard parameters) were used to mitigate scintillation effects on precise point positioning (PPP). To evaluate the effectiveness of MP and ROTI in mitigating scintillation effects, S4 and σϕ were also used for comparison and validation. Three strategies are proposed: (1) remove all observations from the satellite that is most affected by scintillation; (2) remove the scintillation-affected observations; (3) weight the measurement noise matrix in the Kalman Filter (KF) process. The results show that the observation removal and weighting strategies are considerably more effective than the satellite removal strategy. The results also show that the improvement of PPP outputs reaches 93.1% and the performance of standard parameters is comparable to that of scintillation parameters in the observation removal and weighting strategies. [ABSTRACT FROM AUTHOR]

The primary means for electronic position fixing in use in contemporary maritime transport are shipborne GPS (Global Positioning System) receivers or DGPS (Differential GPS) receivers. More advanced GNSS (Global Navigation Satellite System) or RNSS (Regional Navigation Satellite Systems) receivers are able to process combined signals from American GPS, Russian GLONASS, Chinese Beidou (BDS), European Galileo, Indian IRNSS, and Japan QZSS. Satellite-based augmentation systems (SBAS) are still not commonly used in the maritime domain, especially onboard vessels certified under international SOLAS convention. The issues and weaknesses of existing International Maritime Organization recommendations, guidelines, requirements, performance standards, and policies on GNSS shipborne sensors are discussed and presented in the paper. Many problems that have already been dealt with in other means of transportation are still to be solved in the maritime domain. The integrity monitoring is addressed as the main issue, and recommendations based on solutions implemented in aviation and the latest research are proposed. Finally, the strengths, weaknesses, opportunities, and threats awaiting maritime GNSS standardization process are outlined. [ABSTRACT FROM AUTHOR]

China's first Mars exploration mission, named Tianwen-1, landed on Mars on 15 May 2021. The Mars Surface Composition Detector (MarSCoDe) payload onboard the Zhurong rover applied the laser-induced breakdown spectroscopy (LIBS) technique to acquire chemical compositions of Martian rocks and soils. The quantitative interpretation of MarSCoDe-LIBS spectra needs to establish a LIBS spectral database that requires plenty of terrestrial geological standards. In this work, we selected 316 terrestrial standards including igneous rocks, sedimentary rocks, metamorphic rocks, and ores, whose chemical compositions, rock types, and chemical weathering characteristics were comparable to those of Martian materials from previous orbital and in situ detections. These rocks were crushed, ground, and sieved into powders less than <38 μm and pressed into pellets to minimize heterogeneity at the scale of laser spot. The chemical compositions of these standards were independently measured by X-ray fluorescence (XRF). Subsequently, the LIBS spectra of MAL standards were acquired using an established LIBS system at Shandong University (SDU-LIBS). In order to evaluate the performance of these standards in LIBS spectral interpretation, we established multivariate models using partial least squares (PLS) and least absolute shrinkage and selection (LASSO) algorithms to predict the abundance of major elements based on SDU-LIBS spectra. The root mean squared error (RMSE) values of these models are comparable to those of the published models for MarSCoDe, ChemCam, and SuperCam, suggesting these PLS and LASSO models work well. From our research, we can conclude that these 316 MAL targets are good candidates to acquire geochemistry information based on the LIBS technique. These targets could be regarded as geological standards to build a LIBS database using a prototype of MarSCoDe in the near future, which is critical to obtain accurate chemical compositions of Martian rocks and soils based on MarSCoDe-LIBS spectral data. [ABSTRACT FROM AUTHOR]

Point clouds derived using structure from motion (SfM) algorithms from unmanned aerial vehicles (UAVs) are increasingly used in civil engineering practice. This includes areas such as (vegetated) rock outcrops or faces above linear constructions (e.g., railways) where accurate terrain identification, i.e., ground filtering, is highly difficult but, at the same time, important for safety management. In this paper, we evaluated the performance of standard geometrical ground filtering algorithms (a progressive morphological filter (PMF), a simple morphological filter (SMRF) or a cloth simulation filter (CSF)) and a structural filter, CANUPO (CAractérisation de NUages de POints), for ground identification in a point cloud derived by SfM from UAV imagery in such an area (a railway ledge and the adjacent rock face). The performance was evaluated both in the original position and after levelling the point cloud (its transformation into the horizontal plane). The poor results of geometrical filters (total errors of approximately 6–60% with PMF performing the worst) and a mediocre result of CANUPO (approximately 4%) led us to combine these complementary approaches, yielding total errors of 1.2% (CANUPO+SMRF) and 0.9% (CANUPO+CSF). This new technique could represent an excellent solution for ground filtering of high-density point clouds of such steep vegetated areas that can be well-used, for example, in civil engineering practice. [ABSTRACT FROM AUTHOR]

Intertidal seagrass plays a vital role in estimating the overall health and dynamics of coastal environments due to its interaction with tidal changes. However, most seagrass habitats around the globe have been in steady decline due to human impacts, disturbing the already delicate balance in the environmental conditions that sustain seagrass. Miniaturization of multi-spectral sensors has facilitated very high resolution mapping of seagrass meadows, which significantly improves the potential for ecologists to monitor changes. In this study, two analytical approaches used for classifying intertidal seagrass habitats are compared—Object-based Image Analysis (OBIA) and Fully Convolutional Neural Networks (FCNNs). Both methods produce pixel-wise classifications in order to create segmented maps. FCNNs are an emerging set of algorithms within Deep Learning. Conversely, OBIA has been a prominent solution within this field, with many studies leveraging in-situ data and multiresolution segmentation to create habitat maps. This work demonstrates the utility of FCNNs in a semi-supervised setting to map seagrass and other coastal features from an optical drone survey conducted at Budle Bay, Northumberland, England. Semi-supervision is also an emerging field within Deep Learning that has practical benefits of achieving state of the art results using only subsets of labelled data. This is especially beneficial for remote sensing applications where in-situ data is an expensive commodity. For our results, we show that FCNNs have comparable performance with the standard OBIA method used by ecologists. [ABSTRACT FROM AUTHOR]

Persistent scatterer interferometry (PSI) is a group of advanced interferometric synthetic aperture radar (SAR) techniques used to measure and monitor terrain deformation. Sentinel-1 has improved the data acquisition throughout and, compared to previous sensors, increased considerably the differential interferometric SAR (DInSAR) and PSI deformation monitoring potential. The low density of persistent scatterer (PS) in non-urban areas is a critical issue in DInSAR and has inspired the development of alternative approaches and refinement of the PS chains. This paper proposes two different and complementary data-driven procedures to obtain terrain deformation maps. These approaches aim to exploit Sentinel-1 highly coherent interferograms and their short revisit time. The first approach, called direct integration (DI), aims at providing a very fast and straightforward approach to screen-wide areas and easily detects active areas. This approach fully exploits the coherent interferograms from consecutive images provided by Sentinel-1, resulting in a very high sampling density. However, it lacks robustness and its usability lays on the operator experience. The second method, called persistent scatterer interferometry geomatics (PSIG) short temporal baseline, provides a constrained application of the PSIG chain, the CTTC approach to the PSI. It uses short temporal baseline interferograms and does not assume any deformation model for point selection. It is also quite a straightforward approach, which improves the performances of the standard PSIG approach, increasing the PS density and providing robust measurements. The effectiveness of the approaches is illustrated through analyses performed on different test sites. [ABSTRACT FROM AUTHOR]

Artificial Neural Networks (ANNs) have been used in a wide range of applications for complex datasets with their flexible mathematical architecture. The flexibility is favored by the introduction of a higher number of connections and variables, in general. However, over-parameterization of the ANN equations and the existence of redundant input variables usually result in poor test performance. This paper proposes a superstructure-based mixed-integer nonlinear programming method for optimal structural design including neuron number selection, pruning, and input selection for multilayer perceptron (MLP) ANNs. In addition, this method uses statistical measures such as the parameter covariance matrix in order to increase the test performance while permitting reduced training performance. The suggested approach was implemented on two public hyperspectral datasets (with 10% and 50% sampling ratios), namely Indian Pines and Pavia University, for the classification problem. The test results revealed promising performances compared to the standard fully connected neural networks in terms of the estimated overall and individual class accuracies. With the application of the proposed superstructural optimization, fully connected networks were pruned by over 60% in terms of the total number of connections, resulting in an increase of 4% for the 10% sampling ratio and a 1% decrease for the 50% sampling ratio. Moreover, over 20% of the spectral bands in the Indian Pines data and 30% in the Pavia University data were found statistically insignificant, and they were thus removed from the MLP networks. As a result, the proposed method was found effective in optimizing the architectural design with high generalization capabilities, particularly for fewer numbers of samples. The analysis of the eliminated spectral bands revealed that the proposed algorithm mostly removed the bands adjacent to the pre-eliminated noisy bands and highly correlated bands carrying similar information. [ABSTRACT FROM AUTHOR]

Tropospheric delay is a major error source that affects the performance of the Global Navigation Satellite Systems (GNSS) Real Time Kinematic (RTK) positioning especially for the medium/long-range baseline. Although high precision tropospheric delay can be estimated by GNSS carrier phase measurement, together with position and ambiguity, a relatively long period of convergence time is necessary. In this study, we develop a new GPS/BDS RTK algorithm constrained with a tropospheric delay parameters from Numerical weather prediction (NWP) model for medium/long-range baselines. The accuracy of the tropospheric delays derived from NWP is assessed through comparisons with the results of GAMIT (GNSS at MIT). The positioning performance with standard GPS RTK, standard GPS/BDS RTK, the developed NWP-constrained GPS RTK and NWP-constrained GPS/BDS RTK over medium/long-range baselines are compared in terms of the initialization time and the positioning accuracy. Experiment results show that the mean differences between the NWP and GAMIT zenith tropospheric delay (ZTD) are between −5.50 mm and 5.60 mm, and the RMS values of the NWP ZTD residuals are from 24.02 mm to 32.62 mm. A reduction in the initialization time of over 41% and 58% for medium- and long-range baselines can be achieved with the NWP-constrained RTK (both GPS alone and GPS/BDS RTK solutions) compared to the standard RTK solution, respectively. An improvement of over 30% can be found with the GPS/BDS RTK compared with that of the GPS alone RTK for both standard and the NWP-constrained modes. The positioning precision of NWP-constrained GPS/BDS RTK is better than 3 cm in the horizontal direction and better than 5 cm in the vertical direction, which satisfies the requirement of the precise positioning service. [ABSTRACT FROM AUTHOR]