Magnetotelluric (MT) and magnetovariational (MV) sounding are two principal geophysical methods used to determine the electrical structure of the earth using natural electromagnetic signals. The complex relationship between the alternating electromagnetic fields can be defined by transfer functions, and their proper selection is crucial in a 3-D inversion. A synthetic case was studied to assess the capacity of these transfer functions to recover the electrical resistivity distribution of the subsurface and to evaluate the advantages and disadvantages of using the tipper vector W to complement the impedance tensor Z and the phase tensor Φ. The analysis started with two sensitivity tests to appraise the sensitivity of each type of transfer function, which is calculated for an oblique conductor model, showing that the resistivity perturbation of the same model will produce distinct perturbations to different transfer functions; the transfer function sensitivity is significantly different. A 3-D inversion utilizing the quasi-Newton method based on the L-BFGS formula was performed to invert different transfer functions and their combinations, along with quantifying their accuracy. The synthetic case study illustrates that a 3-D inversion of either the Z or Φ responses presents a superior ability to recover the subsurface electrical resistivity; joint inversions of the Z or Φ responses with the W responses possess superior imaging of the horizontal continuity of the conductive block. The appraisal of the 3-D inversion results of different transfer functions can facilitate assessing the advantages of different transfer functions and acquiring a more reasonable interpretation.
{"title":"Appraisal of the Magnetotelluric and Magnetovariational Transfer Functions' Selection in a 3-D Inversion","authors":"Hui Yu, Bin Tang, Juzhi Deng, Hui Chen, Wenwu Tang, Xiao Chen, Cong Zhou","doi":"10.3390/rs15133416","DOIUrl":"https://doi.org/10.3390/rs15133416","url":null,"abstract":"Magnetotelluric (MT) and magnetovariational (MV) sounding are two principal geophysical methods used to determine the electrical structure of the earth using natural electromagnetic signals. The complex relationship between the alternating electromagnetic fields can be defined by transfer functions, and their proper selection is crucial in a 3-D inversion. A synthetic case was studied to assess the capacity of these transfer functions to recover the electrical resistivity distribution of the subsurface and to evaluate the advantages and disadvantages of using the tipper vector W to complement the impedance tensor Z and the phase tensor Φ. The analysis started with two sensitivity tests to appraise the sensitivity of each type of transfer function, which is calculated for an oblique conductor model, showing that the resistivity perturbation of the same model will produce distinct perturbations to different transfer functions; the transfer function sensitivity is significantly different. A 3-D inversion utilizing the quasi-Newton method based on the L-BFGS formula was performed to invert different transfer functions and their combinations, along with quantifying their accuracy. The synthetic case study illustrates that a 3-D inversion of either the Z or Φ responses presents a superior ability to recover the subsurface electrical resistivity; joint inversions of the Z or Φ responses with the W responses possess superior imaging of the horizontal continuity of the conductive block. The appraisal of the 3-D inversion results of different transfer functions can facilitate assessing the advantages of different transfer functions and acquiring a more reasonable interpretation.","PeriodicalId":20944,"journal":{"name":"Remote. Sens.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87283755","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
O. J. Montañez, Marco Javier Suarez, Eduardo Avendano Fernandez
In surveillance and monitoring systems, the use of mobile vehicles or unmanned aerial vehicles (UAVs), like the drone type, provides advantages in terms of access to the environment with enhanced range, maneuverability, and safety due to the ability to move omnidirectionally to explore, identify, and perform some security tasks. These activities must be performed autonomously by capturing data from the environment; usually, the data present errors and uncertainties that impact the recognition and resolution in the detection and identification of objects. The resolution in the acquisition of data can be improved by integrating data sensor fusion systems to measure the same physical phenomenon from two or more sensors by retrieving information simultaneously. This paper uses the constant turn and rate velocity (CTRV) kinematic model of a drone but includes the angular velocity not considered in previous works as a complementary alternative in Lidar and Radar data sensor fusion retrieved using UAVs and applying the extended Kalman filter (EKF) for the detection of moving targets. The performance of the EKF is evaluated by using a dataset that jointly includes position data captured from a LiDAR and a Radar sensor for an object in movement following a trajectory with sudden changes. Additive white Gaussian noise is then introduced into the data to degrade the data. Then, the root mean square error (RMSE) versus the increase in noise power is evaluated, and the results show an improvement of 0.4 for object detection over other conventional kinematic models that do not consider significant trajectory changes.
{"title":"Application of Data Sensor Fusion Using Extended Kalman Filter Algorithm for Identification and Tracking of Moving Targets from LiDAR-Radar Data","authors":"O. J. Montañez, Marco Javier Suarez, Eduardo Avendano Fernandez","doi":"10.3390/rs15133396","DOIUrl":"https://doi.org/10.3390/rs15133396","url":null,"abstract":"In surveillance and monitoring systems, the use of mobile vehicles or unmanned aerial vehicles (UAVs), like the drone type, provides advantages in terms of access to the environment with enhanced range, maneuverability, and safety due to the ability to move omnidirectionally to explore, identify, and perform some security tasks. These activities must be performed autonomously by capturing data from the environment; usually, the data present errors and uncertainties that impact the recognition and resolution in the detection and identification of objects. The resolution in the acquisition of data can be improved by integrating data sensor fusion systems to measure the same physical phenomenon from two or more sensors by retrieving information simultaneously. This paper uses the constant turn and rate velocity (CTRV) kinematic model of a drone but includes the angular velocity not considered in previous works as a complementary alternative in Lidar and Radar data sensor fusion retrieved using UAVs and applying the extended Kalman filter (EKF) for the detection of moving targets. The performance of the EKF is evaluated by using a dataset that jointly includes position data captured from a LiDAR and a Radar sensor for an object in movement following a trajectory with sudden changes. Additive white Gaussian noise is then introduced into the data to degrade the data. Then, the root mean square error (RMSE) versus the increase in noise power is evaluated, and the results show an improvement of 0.4 for object detection over other conventional kinematic models that do not consider significant trajectory changes.","PeriodicalId":20944,"journal":{"name":"Remote. Sens.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78619755","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Wind is the main external force that governs the spreading of river plumes in the sea. Many previous studies demonstrated that the spreading direction of river plumes (especially small plumes) generally coincides with wind direction. At the same time, the majority of river plumes are strongly affected by the Coriolis force, which is also among the baseline knowledge about the plumes. In this study, we focus on the deflection of plumes from wind direction induced by the Coriolis force, which received little attention before. For this purpose, we analyzed an extensive set of Landsat 8 and Sentinel-2 satellite images of multiple small- and medium-sized river plumes at different parts of the World Ocean and synchronous wind reanalysis data. We demonstrated that the deflection angle is stable for individual river plumes for different wind directions, albeit with certain limitations related to wind speed and coastal morphology. Moreover, the deflection angle is similar for river plumes located at similar latitudes and varies from ~0° near the Equator to 15–25° in temperate zones and ~30° in polar zones. Finally, we derived a direct relation between latitude and the deflection angle. The obtained results contribute to our understanding of universal features of river plume dynamics, which is important for monitoring and forecasting of delivery and fate of fluvial water and river-borne matter in different coastal regions of the World Ocean.
{"title":"Influence of the Coriolis Force on Spreading of River Plumes","authors":"A. Osadchiev, Ivan Alfimenkov, V. Rogozhin","doi":"10.3390/rs15133397","DOIUrl":"https://doi.org/10.3390/rs15133397","url":null,"abstract":"Wind is the main external force that governs the spreading of river plumes in the sea. Many previous studies demonstrated that the spreading direction of river plumes (especially small plumes) generally coincides with wind direction. At the same time, the majority of river plumes are strongly affected by the Coriolis force, which is also among the baseline knowledge about the plumes. In this study, we focus on the deflection of plumes from wind direction induced by the Coriolis force, which received little attention before. For this purpose, we analyzed an extensive set of Landsat 8 and Sentinel-2 satellite images of multiple small- and medium-sized river plumes at different parts of the World Ocean and synchronous wind reanalysis data. We demonstrated that the deflection angle is stable for individual river plumes for different wind directions, albeit with certain limitations related to wind speed and coastal morphology. Moreover, the deflection angle is similar for river plumes located at similar latitudes and varies from ~0° near the Equator to 15–25° in temperate zones and ~30° in polar zones. Finally, we derived a direct relation between latitude and the deflection angle. The obtained results contribute to our understanding of universal features of river plume dynamics, which is important for monitoring and forecasting of delivery and fate of fluvial water and river-borne matter in different coastal regions of the World Ocean.","PeriodicalId":20944,"journal":{"name":"Remote. Sens.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85864585","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Recently, unsupervised domain adaptation (UDA) segmentation of remote sensing images (RSIs) has attracted a lot of attention. However, the performance of such methods still lags far behind that of their supervised counterparts. To this end, this paper focuses on a more practical yet under-investigated problem, semi-supervised domain adaptation (SSDA) segmentation of RSIs, to effectively improve the segmentation results of targeted RSIs with a few labeled samples. First, differently from the existing single-prototype mode, a novel cross-domain multi-prototype constraint is proposed, to deal with large inter-domain discrepancies and intra-domain variations. Specifically, each class is represented as a set of prototypes, so that multiple sets of prototypes corresponding to different classes can better model complex inter-class differences, while different prototypes within the same class can better describe the rich intra-class relations. Meanwhile, the multi-prototypes are calculated and updated jointly using source and target samples, which can effectively promote the utilization and fusion of the feature information in different domains. Second, a contradictory structure learning mechanism is designed to further improve the domain alignment, with an enveloping form. Third, self-supervised learning is adopted, to increase the number of target samples involved in prototype updating and domain adaptation training. Extensive experiments verified the effectiveness of the proposed method for two aspects: (1) Compared with the existing SSDA methods, the proposed method could effectively improve the segmentation performance by at least 7.38%, 4.80%, and 2.33% on the Vaihingen, Potsdam, and Urban datasets, respectively; (2) with only five labeled target samples available, the proposed method could significantly narrow the gap with its supervised counterparts, which was reduced to at least 4.04%, 6.04%, and 2.41% for the three RSIs.
{"title":"Cross-Domain Multi-Prototypes with Contradictory Structure Learning for Semi-Supervised Domain Adaptation Segmentation of Remote Sensing Images","authors":"Kuiliang Gao, Anzhu Yu, Xiong You, C. Qiu, Bing Liu, Fubing Zhang","doi":"10.3390/rs15133398","DOIUrl":"https://doi.org/10.3390/rs15133398","url":null,"abstract":"Recently, unsupervised domain adaptation (UDA) segmentation of remote sensing images (RSIs) has attracted a lot of attention. However, the performance of such methods still lags far behind that of their supervised counterparts. To this end, this paper focuses on a more practical yet under-investigated problem, semi-supervised domain adaptation (SSDA) segmentation of RSIs, to effectively improve the segmentation results of targeted RSIs with a few labeled samples. First, differently from the existing single-prototype mode, a novel cross-domain multi-prototype constraint is proposed, to deal with large inter-domain discrepancies and intra-domain variations. Specifically, each class is represented as a set of prototypes, so that multiple sets of prototypes corresponding to different classes can better model complex inter-class differences, while different prototypes within the same class can better describe the rich intra-class relations. Meanwhile, the multi-prototypes are calculated and updated jointly using source and target samples, which can effectively promote the utilization and fusion of the feature information in different domains. Second, a contradictory structure learning mechanism is designed to further improve the domain alignment, with an enveloping form. Third, self-supervised learning is adopted, to increase the number of target samples involved in prototype updating and domain adaptation training. Extensive experiments verified the effectiveness of the proposed method for two aspects: (1) Compared with the existing SSDA methods, the proposed method could effectively improve the segmentation performance by at least 7.38%, 4.80%, and 2.33% on the Vaihingen, Potsdam, and Urban datasets, respectively; (2) with only five labeled target samples available, the proposed method could significantly narrow the gap with its supervised counterparts, which was reduced to at least 4.04%, 6.04%, and 2.41% for the three RSIs.","PeriodicalId":20944,"journal":{"name":"Remote. Sens.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76263390","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Shaoqiang Chang, Fawei Yang, Zhennan Liang, Wei Ren, H. Zhang, Quanhua Liu
This paper proposed a Pulse-Agile-Phase-Coding slow-time MIMO (PAPC-st-MIMO) waveform, where the phase-coded signal is utilized as the intra-pulse modulation of the slow-time MIMO waveform. Firstly, the signal model of the proposed waveform is derived. To improve the orthogonality of the phase-coded waveform sets, a novel hybrid evolutionary algorithm based on Cyclic Algorithm New (CAN) is proposed. After the optimization process of the phase-coded waveform sets, the signal processing method of the PAPC-st-MIMO waveform is derived. Finally, the effectiveness of the proposed method is verified with a simulation experiment. The mitigation ratio of the near-range detection waveform can achieve −30 dB, while the long-range detection waveform can achieve −35 dB. This approach ensures waveform orthogonality while enabling the slow-time MIMO waveform to achieve distance selectivity. By conducting joint pulse-Doppler processing across multiple range segments, range ambiguity can be suppressed, increasing the system’s Pulse Repetition Frequency (PRF) without introducing ambiguity.
{"title":"Slow-Time MIMO Waveform Design Using Pulse-Agile-Phase-Coding for Range Ambiguity Mitigation","authors":"Shaoqiang Chang, Fawei Yang, Zhennan Liang, Wei Ren, H. Zhang, Quanhua Liu","doi":"10.3390/rs15133395","DOIUrl":"https://doi.org/10.3390/rs15133395","url":null,"abstract":"This paper proposed a Pulse-Agile-Phase-Coding slow-time MIMO (PAPC-st-MIMO) waveform, where the phase-coded signal is utilized as the intra-pulse modulation of the slow-time MIMO waveform. Firstly, the signal model of the proposed waveform is derived. To improve the orthogonality of the phase-coded waveform sets, a novel hybrid evolutionary algorithm based on Cyclic Algorithm New (CAN) is proposed. After the optimization process of the phase-coded waveform sets, the signal processing method of the PAPC-st-MIMO waveform is derived. Finally, the effectiveness of the proposed method is verified with a simulation experiment. The mitigation ratio of the near-range detection waveform can achieve −30 dB, while the long-range detection waveform can achieve −35 dB. This approach ensures waveform orthogonality while enabling the slow-time MIMO waveform to achieve distance selectivity. By conducting joint pulse-Doppler processing across multiple range segments, range ambiguity can be suppressed, increasing the system’s Pulse Repetition Frequency (PRF) without introducing ambiguity.","PeriodicalId":20944,"journal":{"name":"Remote. Sens.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81101412","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Multipath error is an important factor restricting the relative positioning accuracy of the Beidou Navigation Satellite System (BDS). Because of the complexity of the reflection environment, the mathematical modeling of multipath errors is quite difficult. The sidereal filtering algorithm corrects multipath errors by using the feature of period repetition, which can greatly reduce its influence and improve the accuracy of positioning and attitude measurement. In view of the constellation heterogeneity of BDS, it is more complicated to apply sideral filtering. Based on the reconstructed single-difference residual of the carrier phase, the multipath repetition time of the Beidou satellite is estimated using the idea of segmentation. The Tikhonov regularization method and the classical wavelet method are used to extract the multipath of the single-difference residual of the carrier phase, and the “clean” sequence of the single-difference residual is obtained. The experimental results show that it is feasible to extract the multipath error correctly by Tikhonov regularization, and the multipath error is smoother than the original residual measurement. Furthermore, the estimation method of the regularization parameter is further optimized. After using the optimized Tikhonov regularization method with sidereal filtering, the mean RMS improvements of GEO, IGSO, and MEO satellites are 45.9%, 38.2%, and 37.5%, respectively. The positioning accuracy on E, N, and U components is improved by 24.8%, 26.3%, and 42.7%, respectively. The attitude resolution accuracy is improved by 22.9% in the yaw angle and 12.6% in the pitch angle. The proposed method can be an alternative BDS multipath error modeling and mitigation approach.
{"title":"A Multipath Error Reduction Method for BDS Using Tikhonov Regularization with Parameter Optimization","authors":"Xinzhong Li, Yongliang Xiong, Shaoguang Xu, Weiwei Chen, Ban Zhao, Rui Zhang","doi":"10.3390/rs15133400","DOIUrl":"https://doi.org/10.3390/rs15133400","url":null,"abstract":"Multipath error is an important factor restricting the relative positioning accuracy of the Beidou Navigation Satellite System (BDS). Because of the complexity of the reflection environment, the mathematical modeling of multipath errors is quite difficult. The sidereal filtering algorithm corrects multipath errors by using the feature of period repetition, which can greatly reduce its influence and improve the accuracy of positioning and attitude measurement. In view of the constellation heterogeneity of BDS, it is more complicated to apply sideral filtering. Based on the reconstructed single-difference residual of the carrier phase, the multipath repetition time of the Beidou satellite is estimated using the idea of segmentation. The Tikhonov regularization method and the classical wavelet method are used to extract the multipath of the single-difference residual of the carrier phase, and the “clean” sequence of the single-difference residual is obtained. The experimental results show that it is feasible to extract the multipath error correctly by Tikhonov regularization, and the multipath error is smoother than the original residual measurement. Furthermore, the estimation method of the regularization parameter is further optimized. After using the optimized Tikhonov regularization method with sidereal filtering, the mean RMS improvements of GEO, IGSO, and MEO satellites are 45.9%, 38.2%, and 37.5%, respectively. The positioning accuracy on E, N, and U components is improved by 24.8%, 26.3%, and 42.7%, respectively. The attitude resolution accuracy is improved by 22.9% in the yaw angle and 12.6% in the pitch angle. The proposed method can be an alternative BDS multipath error modeling and mitigation approach.","PeriodicalId":20944,"journal":{"name":"Remote. Sens.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81964056","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
It is a widespread assumption that burned area and severity are increasing worldwide due to climate change. This issue has motivated former analysis based on satellite imagery, revealing a decreasing trend in global burned areas. However, few studies have addressed burn severity trends, rarely relating them to climate variables, and none of them at the global scale. Within this context, we characterized the spatiotemporal patterns of burned area and severity by biomes and continents and we analyzed their relationships with climate over 17 years. African flooded and non-flooded grasslands and savannas were the most fire-prone biomes on Earth, whereas taiga and tundra exhibited the highest burn severity. Our temporal analysis updated the evidence of a decreasing trend in the global burned area (−1.50% year−1; p < 0.01) and revealed increases in the fraction of burned area affected by high severity (0.95% year−1; p < 0.05). Likewise, the regions with significant increases in mean burn severity, and burned areas at high severity outnumbered those with significant decreases. Among them, increases in severely burned areas in the temperate broadleaf and mixed forests of South America and tropical moist broadleaf forests of Australia were particularly intense. Although the spatial patterns of burned area and severity are clearly driven by climate, we did not find climate warming to increase burned area and burn severity over time, suggesting other factors as the primary drivers of current shifts in fire regimes at the planetary scale.
{"title":"Global Patterns and Dynamics of Burned Area and Burn Severity","authors":"V. Fernández-García, E. Alonso‐González","doi":"10.3390/rs15133401","DOIUrl":"https://doi.org/10.3390/rs15133401","url":null,"abstract":"It is a widespread assumption that burned area and severity are increasing worldwide due to climate change. This issue has motivated former analysis based on satellite imagery, revealing a decreasing trend in global burned areas. However, few studies have addressed burn severity trends, rarely relating them to climate variables, and none of them at the global scale. Within this context, we characterized the spatiotemporal patterns of burned area and severity by biomes and continents and we analyzed their relationships with climate over 17 years. African flooded and non-flooded grasslands and savannas were the most fire-prone biomes on Earth, whereas taiga and tundra exhibited the highest burn severity. Our temporal analysis updated the evidence of a decreasing trend in the global burned area (−1.50% year−1; p < 0.01) and revealed increases in the fraction of burned area affected by high severity (0.95% year−1; p < 0.05). Likewise, the regions with significant increases in mean burn severity, and burned areas at high severity outnumbered those with significant decreases. Among them, increases in severely burned areas in the temperate broadleaf and mixed forests of South America and tropical moist broadleaf forests of Australia were particularly intense. Although the spatial patterns of burned area and severity are clearly driven by climate, we did not find climate warming to increase burned area and burn severity over time, suggesting other factors as the primary drivers of current shifts in fire regimes at the planetary scale.","PeriodicalId":20944,"journal":{"name":"Remote. Sens.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77755329","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Jingshi Wang, X. Zhuge, Fengjiao Chen, Xu Chen, Yuan Wang
The northeastern China cold vortex (NCCV) is the main weather system affecting Northeast China. Based on the precipitation products from the dual-frequency precipitation radar (DPR) onboard the Global Precipitation Measurement core observatory (GPM) satellite, the precipitation structures and microphysical properties for different rain types in 6432 NCCVs from 2014 to 2019 were studied using dynamic composite analysis. Our results show that the precipitation in NCCVs is dominated by stratiform precipitation. Regions with high stratiform and convective precipitation frequency have a comma shape. The growth mechanism of precipitation particles changes at ~4 km in altitude, the lower particles grow through collision (more pronounced in convective precipitation), and the upper hydrometeors grow through the Bergeron process. Additionally, the precipitation structures and microphysical properties exhibit great regional variations in NCCVs. The rainfall for all rain types is the strongest in the southeast region within an NCCV, mainly characterized by higher near-surface droplet concentration, while precipitation events occur more frequently in the southeast region for all rain types. There are active rimming growth processes above the melting layer for convective precipitation in the western region of an NCCV. In the southeast region of an NCCV, the collision growth of droplets in both types of precipitation is the most obvious.
{"title":"A Preliminary Analysis of Typical Structures and Microphysical Characteristics of Precipitation in Northeastern China Cold Vortexes","authors":"Jingshi Wang, X. Zhuge, Fengjiao Chen, Xu Chen, Yuan Wang","doi":"10.3390/rs15133399","DOIUrl":"https://doi.org/10.3390/rs15133399","url":null,"abstract":"The northeastern China cold vortex (NCCV) is the main weather system affecting Northeast China. Based on the precipitation products from the dual-frequency precipitation radar (DPR) onboard the Global Precipitation Measurement core observatory (GPM) satellite, the precipitation structures and microphysical properties for different rain types in 6432 NCCVs from 2014 to 2019 were studied using dynamic composite analysis. Our results show that the precipitation in NCCVs is dominated by stratiform precipitation. Regions with high stratiform and convective precipitation frequency have a comma shape. The growth mechanism of precipitation particles changes at ~4 km in altitude, the lower particles grow through collision (more pronounced in convective precipitation), and the upper hydrometeors grow through the Bergeron process. Additionally, the precipitation structures and microphysical properties exhibit great regional variations in NCCVs. The rainfall for all rain types is the strongest in the southeast region within an NCCV, mainly characterized by higher near-surface droplet concentration, while precipitation events occur more frequently in the southeast region for all rain types. There are active rimming growth processes above the melting layer for convective precipitation in the western region of an NCCV. In the southeast region of an NCCV, the collision growth of droplets in both types of precipitation is the most obvious.","PeriodicalId":20944,"journal":{"name":"Remote. Sens.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77982058","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Recently, the rapid development of deep learning has greatly improved the performance of image classification. However, a central problem in hyperspectral image (HSI) classification is spectral uncertainty, where spectral features alone cannot accurately and robustly identify a pixel point in a hyperspectral image. This paper presents a novel HSI classification network called MS-RPNet, i.e., multiscale superpixelwise RPNet, which combines superpixel-based S3-PCA with two-dimensional singular spectrum analysis (2D-SSA) based on the Random Patches Network (RPNet). The proposed frame can not only take advantage of the data-driven method, but can also apply S3-PCA to efficiently consider more global and local spectral knowledge at the super-pixel level. Meanwhile, 2D-SSA is used for noise removal and spatial feature extraction. Then, the final features are obtained by random patch convolution and other steps according to the cascade structure of RPNet. The layered extraction superimposes the different sparial information into multi-scale spatial features, which complements the features of various land covers. Finally, the final fusion features are classified by SVM to obtain the final classification results. The experimental results in several HSI datasets demonstrate the effectiveness and efficiency of MS-RPNet, which outperforms several current state-of-the-art methods.
{"title":"Hyperspectral Image Classification Based on Fusing S3-PCA, 2D-SSA and Random Patch Network","authors":"Huayue Chen, Tingting Wang, Tao Chen, Wu Deng","doi":"10.3390/rs15133402","DOIUrl":"https://doi.org/10.3390/rs15133402","url":null,"abstract":"Recently, the rapid development of deep learning has greatly improved the performance of image classification. However, a central problem in hyperspectral image (HSI) classification is spectral uncertainty, where spectral features alone cannot accurately and robustly identify a pixel point in a hyperspectral image. This paper presents a novel HSI classification network called MS-RPNet, i.e., multiscale superpixelwise RPNet, which combines superpixel-based S3-PCA with two-dimensional singular spectrum analysis (2D-SSA) based on the Random Patches Network (RPNet). The proposed frame can not only take advantage of the data-driven method, but can also apply S3-PCA to efficiently consider more global and local spectral knowledge at the super-pixel level. Meanwhile, 2D-SSA is used for noise removal and spatial feature extraction. Then, the final features are obtained by random patch convolution and other steps according to the cascade structure of RPNet. The layered extraction superimposes the different sparial information into multi-scale spatial features, which complements the features of various land covers. Finally, the final fusion features are classified by SVM to obtain the final classification results. The experimental results in several HSI datasets demonstrate the effectiveness and efficiency of MS-RPNet, which outperforms several current state-of-the-art methods.","PeriodicalId":20944,"journal":{"name":"Remote. Sens.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81764712","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper introduces a novel technique that uses observation data from GNSS to estimate the ionospheric vertical total electron content (VTEC) using the Kriging–Kalman method. The technique provides a method to validate the accuracy of the Ionospheric VTEC analysis within the Equatorial Ionization anomaly region. The technique developed uses GNSS VTEC alongside solar parameters, such as solar radio flux (F10.7 cm), Disturbance Storm Time (Dst) and other data, and Long Short Term Memory (LSTM) Networks to predict the occurrence time of the ionospheric equatorial anomaly and ionospheric VTEC changes. The LSTM method was applied to GNSS data from Haikou Station. A comparison of this technique with the neural network (NN) model and International Reference Ionosphere model shows that the LSTM outperforms all of them at VTEC estimation and prediction. The results, which are based on the root mean square error (RMSE) between GNSS VTEC and GIM VTEC outside the equatorial anomaly region, was 1.42 TECU, and the results of GNSS VTEC and VTEC from Beidou geostationary orbit satellite, which lies inside the equatorial ionization anomaly region, was 1.92 TECU. The method developed can be used in VTEC prediction and estimation in real time space operations.
{"title":"A Novel Technique for High-Precision Ionospheric VTEC Estimation and Prediction at the Equatorial Ionization Anomaly Region: A Case Study over Haikou Station","authors":"Haining Wang, Qinglin Zhu, Xiang Dong, Dongsheng Sheng, Yong-feng Zhi, Chen Zhou, Bin Xu","doi":"10.3390/rs15133394","DOIUrl":"https://doi.org/10.3390/rs15133394","url":null,"abstract":"This paper introduces a novel technique that uses observation data from GNSS to estimate the ionospheric vertical total electron content (VTEC) using the Kriging–Kalman method. The technique provides a method to validate the accuracy of the Ionospheric VTEC analysis within the Equatorial Ionization anomaly region. The technique developed uses GNSS VTEC alongside solar parameters, such as solar radio flux (F10.7 cm), Disturbance Storm Time (Dst) and other data, and Long Short Term Memory (LSTM) Networks to predict the occurrence time of the ionospheric equatorial anomaly and ionospheric VTEC changes. The LSTM method was applied to GNSS data from Haikou Station. A comparison of this technique with the neural network (NN) model and International Reference Ionosphere model shows that the LSTM outperforms all of them at VTEC estimation and prediction. The results, which are based on the root mean square error (RMSE) between GNSS VTEC and GIM VTEC outside the equatorial anomaly region, was 1.42 TECU, and the results of GNSS VTEC and VTEC from Beidou geostationary orbit satellite, which lies inside the equatorial ionization anomaly region, was 1.92 TECU. The method developed can be used in VTEC prediction and estimation in real time space operations.","PeriodicalId":20944,"journal":{"name":"Remote. Sens.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77708181","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：