Table of Content

20 November 2022, Volume 51 Issue 11

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Geodesy and Navigation

A two-step integral method for geoid determination using generalized band-limited airborne vector gravity data

HUANG Motao, DENG Kailiang, WU Taiqi, WANG Weiping, OUYANG Yongzhong, CHEN Xin, WANG Xu

2022, 51(11):  2245-2254.  doi:10.11947/j.AGCS.2022.20210222

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According to the generalized band-limited horizon boundary value theory of physical geodesy survey, a two-step integral method is proposed to determine the geoid based on the horizontal component of band-limited airborne vector gravity data. Firstly,based on the solution of generalized band-limited horizon boundary value problem, we transform the horizontal component of band-limited airborne vector gravity data into a band-limited disturbing gravity potential at flight level. Secondly, the band-limited disturbing gravity potential is continued downward to the sea level by using the solution of the band-limited Dirichlet boundary value problem. Finally, according to the Bruns formula, the band-limited disturbing gravity potential at sea level is converted to the geoid. Simulation experiments using EGM2008 gravity field model show that the solution of generalized band-limited horizontal boundary value problem has a good low-pass filtering characteristic, which can weaken the effect of high-frequency noises in the observed value. The accuracy of the band-limited geoid obtained by the solution to the band-limited Dirichlet boundary problem is better than 3 cm when the error of the horizontal component of band-limited airborne vector gravity data is taken as 3 mGal and the flying height as 6 km. It indicates that the two-step integral method has good stability and effectiveness.

Short-term GNSS network solution and performance in large height difference region with tropospheric delay constraint

JIANG Guangwei, WANG Panlong, GUO Chunxi, WANG Bin, YANG Yuanxi

2022, 51(11):  2255-2264.  doi:10.11947/j.AGCS.2022.20210448

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The observation of GNSS monitoring in mountain area is often severely restricted by tropospheric delay, resulting in the reduction of positioning accuracy. In this paper, a constraint method for the prior information of tropospheric delay at ground points is put forward to realize the fast and high-precision double differenced observation of the peak. This method can make full use of the augmentation information of tropospheric delay of long-distance ground station, the observation duration decreased notably,increase the stations range of mountain monitoring, so as to reduce costs of mountain measurement task. In this paper, the applicability is verified by the spatio-temporal characteristics of observation duration, baseline distance and number of ground stations. The experimental results indicate that takes the high-precision tropospheric delay into account as prior constraints, acquired by long-time GNSS observation at ground stations, which can weaken the influence of tropospheric delay residuals efficiently induced by large heights different between different stations and improve success rate of ambiguity fixing, and can achieve tropospheric delay parameter estimation and fast high-precision positioning of peak monitoring points. Compared with the unconstrained method, the accuracy of each coordinate component is improved after the troposphere prior constraints, and the accuracy of the elevation direction is improved significantly.

Multi-GNSS RTK/INS tightly coupled integrated navigation method considering ISB/IFB estimation

HAO Yushi, SUN Jianwei, SUI Xin, XU Aigong, SHI Chuang

2022, 51(11):  2265-2272.  doi:10.11947/j.AGCS.2022.20210124

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For better complementarity among multi-GNSS in GNSS challenging environment, multi-GNSS RTK/INS tightly coupled integration with consideration of ISB/IFB estimation is proposed, aiming at ambiguity resolution (AR) failure subject to signal difference between GNSS. Multi-GNSS RTK/INS tightly coupled measurement equation with ISB/IFB parameters is established, and the ISB/IFB estimation algorithm using robust estimation and particle swarm optimization (PSO) is proposed. The experimental results demonstrate that ambiguity-fixing success rate can be increased to a certain extend considering ISB/IFB estimation. Inter-GNSS ambiguity-fixing success rate is related to the accuracy of float solutions, and adopting measurement noise parameter adaptive control to improve float state estimation accuracy can leads better performance in terms of multi-GNSS RTK/INS inter-GNSS AR and navigation accuracy.

CLAMBDA method with additional scaling factor for GNSS ambiguity resolution

LU Liguo, MA Liye, LIU Wanke, WU Tangting, HU Weijian

2022, 51(11):  2273-2284.  doi:10.11947/j.AGCS.2022.20210185

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Under the weak global navigation satellite system (GNSS) model, the initial search space of constrained LAMBDA (CLAMBDA) is too large, which leads to the problem of low ambiguity resolution efficiency. In the paper, an adaptive adjust search space for bounding functions search and shrink strategy (ASS) algorithm based on scaling factor is proposed, and the influence of setting the initial space with upper and lower boundaries on ASS algorithm is discussed. Then the performance of ASS algorithm is experimentally verified through two sets of measured examples. The results show that ASS algorithm with scaling factor can effectively adjust the search space and improve the search efficiency. Besides, setting the initial space based on the lower boundary can further improve the performance of ASS algorithm.

Calculation of VGOS antenna horizon mask and its impact on UT1 measurement

WU De, SHU Fengchun, LI Jinling, ZHONG Shengjian, GAN Jiangying

2022, 51(11):  2285-2293.  doi:10.11947/j.AGCS.2022.20210314

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The two newly-built VGOS (VLBI global observing system) antennas in Shanghai are blocked by the legacy large antennas nearby. In order to carry out joint observation using the VGOS station network, a new method for calculating the horizon mask based on the geometric position of the antennas is proposed. The horizon mask between the two antennas can be calculated from the geometric position, the antenna aperture and the distance from antenna reference point to the edge of the reflector. The maximum shielding angle of Tianma VGOS antenna is 22.3°, and the ratio between the shielded sky area and the sky area above 5° elevation is 2.19%. The maximum shielding angle of the Sheshan VGOS antenna is 23.4°, and the ratio between the shielded sky area and the sky area above 5° elevation is 3.14%. By adding the horizon mask of the antenna in the scheduling, the joint measurement of the VGOS station network was successfully implemented. The performance of UT1 measurement accuracy of the VGOS antenna on the Tianma-Wettzell baseline is simulated and analyzed by VieVS software. The result shows that the horizon mask of the Tianma VGOS antenna has no significant effect on the measurement accuracy of UT1.

Automatic recognition and cleaning of outliers in multi-beam bathymetric data with clustering algorithm

WEI Yuan, JIN Shaohua, LI Shujun, WANG Lei, BIAN Gang, WANG Mo

2022, 51(11):  2294-2302.  doi:10.11947/j.AGCS.2022.20210113

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Eliminating the invalid data and locating the doubtful data are realized by using the algorithm of density clustering, by referring to the image features of outliers in the multi-beam rear view from the perspective of manual interactive processing.The multi-beam bathymetric data are automatically divided into three types: credible data, doubtful data and invalid data by clustering algorithm. The reliable data are retained, invalid data are automatically eliminated, and the doubtful data are judged manually.This kind of automatic outlier cleaning algorithm with partial manual intervention can solve the contradiction between low reliability of automatic processing algorithm and low efficiency of manual interactive processing.An example shows that the algorithm improves the reliability of the results obtained from the automatic processing algorithm to a certain extent, and also,the algorithm is of great significance for the realization of high reliability and high efficiency cleaning of outliers in multi-beam bathymetric data.

The SUT method for precision estimation of mixed additive and multiplicative random error model

WANG Leyang, CHEN Tao

2022, 51(11):  2303-2316.  doi:10.11947/j.AGCS.2022.20200514

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The existing parameter estimation method of mixed additive and multiplicative random error model can achieve second-order precision, but the precision estimation method can only achieve first-order precision. If the traditional Taylor series expansion approximate function method is used to obtain the second-order precision information of parameter estimations, it will inevitably require complicated derivation operation due to the complex nonlinear relationship between parameter estimations and observations in the mixed additive and multiplicative random error model. Aiming at this problem, this paper uses the scaled unscented transformation method, which does not require derivative operation and understand the composition of nonlinear function, to obtain the second-order precision information of parameter estimations. The results of experiments show that using the SUT method to solve the mixed additive and multiplicative random error model can effectively avoid complicated derivation operation, and the obtained parameter estimations and covariance matrix can both achieve second-order precision, thus verifies the feasibility and advantages of the proposed method in this paper.

The method and application for solving separable nonlinear least squares based on matrix decomposition

WANG Luyao, LIU Guolin, WANG Fengyun, WANG Ke, HAN Yu

2022, 51(11):  2317-2327.  doi:10.11947/j.AGCS.2022.20200502

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For the special structure of separable function models, the variable projection (VP) method is used to separate the linear and the nonlinear parameters in this paper, and respectively combined with the full-rank decomposition, QR decomposition, singular value decomposition and Gram-Schmidt orthogon-alization to calculate the parameters, which shorten the calculation time of solving equations by computer, and enable the algorithm more efficient and equations with a certain ill-conditioning maintain the stability in the process of solving. The superiority-inferiority of the algorithms based on different matrix decomposition methods is analyzed by Mackey-Glass time series fitting experiment and parameters calculation for spatial rectangular coordinate transformation. The experimental results show that the improved VP algorithms based on matrix decomposition are highly efficient and stable, and suitable to solve the parameters of spatial rectangular coordinate transformation models.

Analysis of inter-frequency bias in multi-mode multi-frequency GNSS-IR water level retrieval and correction method

WANG Xiaolei, HE Xiufeng, SONG Minfeng, CHEN Shu, NIU Zijin

2022, 51(11):  2328-2338.  doi:10.11947/j.AGCS.2022.20210461

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Accurate water level monitoring is very important for water resources regulation, flood monitoring, and climate and meteorological research. In recent years, with the continuous development of GNSS, one technique named GNSS-interferometry reflectometry (GNSS-IR) water level retrieval has been proposed. Currently, three types of errors—height variation error, elevation bending error and tropospheric delay error have been discovered, and related correction methods have also been proposed. After analysis of the multi-mode and multi-frequency retrievals of four GNSS stations (HKQT, SW50, SW51 and SW52), this paper found that there is also an obvious inter-frequency bias in retrievals in addition to the three types of errors. Previous research has few studies on GNSS-IR inter-frequency bias, and there is no consensus that it is classified as a GNSS-IR error source. In order to further dig out the characteristics of the inter-frequency bias and deepen the understanding of this error, this paper calculated the reflector height (RH) of different signals, and found that there were deviations between the RH values of different frequency signals. Comparing these deviations with the corresponding wavelengths, we found that these deviations have a significant linear relationship with the signal wavelengths (correlation coefficient>95%). The magnitude of these deviations were in a decimeter level, which showed system characteristics of obvious inter-frequency bias. Based on the characteristics of the inter-frequency bias, this paper proposed an error correction method. The results showed that root mean squared errors (RMSEs) of the correction retrievals with consideration of the inter-frequency deviation were 1.5 to 12 cm higher than these without consideration. And the accuracy of the corrected combined retrievals was 30%~80% higher than the retrievals of the uncorrected individual signals. The improvement of accuracy benefits from the large amount of redundant data provided by the multi-mode and multi-frequency signals and the correct handling of various errors including systematic errors, gross errors and random errors.

Establishment and analysis of a refinement method for the GNSS empirical weighted mean temperature model

YANG Fei, GUO Jiming, CHEN Ming, ZHANG Di

2022, 51(11):  2339-2345.  doi:10.11947/j.AGCS.2022.20210269

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The weighted mean temperature (T_m) as a key parameter for the conversion of tropospheric wet delay to precipitable water vapor, plays an important role in the field of GNSS meteorology. Several empirical T_m models were established, which can provide T_m estimates by using the location and time information of the site as input parameters. However, the accuracy of these models is often limited, especially in some local areas. This paper proposed a refinement method for the empirical models, which introduced surface temperature, obtained the refined coefficient by using least squares and achieved the error compensation effect for estimating T_m. Based on the 2011—2015 data of 180 radiosonde sites in China and its nearby regions, this paper carried out the establishment and analysis of the GPT3 refined model. Numerical results show that the GPT3 refined model outperformed the other three models, and improved the T_m accuracy by 16.2%, 13.5% and 21.1% compared with the Bevis model, regional linear model and GPT3 model, respectively. In addition, the T_m estimated by the GPT3 refined model appeared the best spatio-temporal distribution, which significantly improved the accuracy of T_m estimated by other models in high latitudes, and effectively solved the defect that the GPT3 model can only describe the seasonal variation of T_m. The formula of the proposed method is simple, which can be quickly extended to any empirical T_m model.

Photogrammetry and Remote Sensing

Automatic generation DSM of UAV image based on random propagation COLVLL algorithm

ZHANG Chunsen, GE Yingwei, GUO Bingxuan, ZHANG Yueying

2022, 51(11):  2346-2354.  doi:10.11947/j.AGCS.2022.20210325

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In view of the poor performance of existing dense matching methods in weak texture areas and areas with large height differences, and the loss of information when the dense matching results are fused to generate DSM, a DSM generation method based on random propagation COLVLL is proposed. Based on the effective image pair screening of the images after aerial triangulation photogrammetry, the random propagation mechanism is used to scan and iterate the DSM pixel area, combined with the VLL algorithm to iteratively update the randomly generated elevation value to obtain the DSM. Taking the UAV image with weak texture and large elevation difference as the experimental data, compare with the commercial software for generating DSM, and use the Vaihingen data set provided by ISPRS WGII/4 as a reference to test and analyze the DSM and real radiographic data generated by the method in this paper. The results show the effectiveness and applicability of the proposed method.

Semantic segmentation of aerial image based on semi-supervised network with multi-scale shared coding

LI Jiatian, YANG Ruchun, YAO Yanji, HE Rixing, A Xiaohui, LÜ Shaoyun

2022, 51(11):  2355-2364.  doi:10.11947/j.AGCS.2022.20200522

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In semi-supervised semantic segmentation, the segmentation accuracy of aerial images is mainly improved by using the structure of encoder—master-auxiliary decoder which applies the unlabeled samples to the calculation. However, the loss of shallow detail features which is caused by continuous downsampling in the process of encoding makes the boundary of ground objects incomplete. Therefore, a semi-supervised network combining multi-scale shared encoding is proposed for semantic segmentation of aerial images. The encoder uses ResNet-50 to obtain the shallow features of the image, and links the shallow features by embedding a multi-scale shared coding module at the end of ResNet-50 to build a dense feature pyramid and expand the receptive field, thereby obtaining multi-scale detailed information of the target feature. The effectiveness of the proposed method is verified by compared with UNet, DeepLabv3+, FCN and CCT, XModalNet, VLCNet on the two datasets of LandCover.ai and DroneDeploy, and the result shows that our network has obvious advantages in terms of label number and accuracy. For the LandCover.ai dataset, under the premise of 6000 labeled samples and 6500 unlabeled samples, the overall mIoU increased by 1.15%. For the DroneDeploy dataset, under the premise of 30 labeled samples and 5 unlabeled samples, the overall mIoU increased by 0.94%, while significantly improving the segmentation accuracy of ground objects to obtain a clear and complete ground boundary.

Foreground feature manifold ranking method for SAR image change detection

LUO Qingli, CUI Fengzhi, WEI Jujie, MING Lei

2022, 51(11):  2365-2378.  doi:10.11947/j.AGCS.2022.20200512

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There are two problems with the difference image analysis for the current SAR image change detection methods. Some of the changed areas in the connected area are easily misclassified as unchanged areas, and the central prior assumption cannot be well applied to detecting the changed regions located at the boundary of the SAR image. In order to avoid the above limitations, a method of manifold ranking based on superpixel segmentation and foreground features for change detection (MRSFCD) was designed. Firstly, the difference image was constructed by weighted fusion of single pixel and neighborhood logarithmic ratio operator, which can maintain consistency within the change areas and suppress noise interference. The difference image was then segmented by the superpixel model. After that, the improved undirected graph connection method of superpixels was proposed. The main idea is that superpixels on the boundary are not considered when connecting, and superpixel segmentation results and grayscale information are applied for three adjacencies. Finally, we do a dot product between the significance image by manifold ranking based on foreground features and the single-pixel logarithmic difference image, and the final binary change image is obtained by threshold segmentation. In this paper, three datasets of dual-phase images are tested. The results indicate that compared with other change detection algorithms, the proposed method can improve the accuracy of change detection effectively.

Cartography and Geoinformation

Reversible watermarking for vector maps based on interval mapping and maximum perturbation region

XI Xu, ZHANG Xinchang

2022, 51(11):  2379-2389.  doi:10.11947/j.AGCS.2022.20210552

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In the traditional reversible watermarking for vector maps, the watermark is often at the risk of being over exposed as a result to the one-sided pursuit of watermark embedding capacity. To address this problem, a reversible watermarking algorithm with controlled perturbation degree is proposed in this paper. Firstly, the state interval axes of the coordinate points are designed based on the idea of quantized index modulation, while the state values of coordinate points are modulated by the watermark information, all to guarantee a successful embedding of watermarks. Secondly, to control the impact on the data quality by the embedding of watermarks, the maximum perturbation region method is introduced to confine the state interval of coordinate points. Finally, to upgrade the watermark capacity, the binary watermark information is decimalized to modulate the state value of coordinate points in the maximum perturbation region. The experimental results have been promising, according to which the proposed reversible watermarking for vector maps not only has an outstanding capacity and a controllable perturbation degree, but also demonstrates strong robustness against common geometric attacks and point attacks, thus strikes a great balance between watermark capacity, invisibility, and robustness.

Graph convolution neural network method for shape classification of areal settlements

YU Yangyang, HE Kangjie, WU Fang, XU Junkui

2022, 51(11):  2390-2402.  doi:10.11947/j.AGCS.2022.20210134

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Shape recognition and classification is one of the important contents of cartographic generalization. Areal settlement is an important part of geospatial vector data and its shape cognition is a basic technique of cartographic generalization. To solve the shortcomings of traditional geometric and statistical shape classification methods, this paper proposes a novel areal settlements shape classification method based on graph data classification ability of graph convolutional neural network. Firstly, the computation graph is generated according to the contour polygon of areal settlement, and the features of the contour shape are extracted as the attributes of the vertices of computation graph. Secondly, the vertex attributes of the computation graph are aggregated and transmitted for multiple rounds, and the shape information is embedded into a high dimension vector with these vertices attributes. Finally, the graph vectors are input into a fully connected neural network to realize the classification of graphs. The experimental results show that this method can effectively achieve the end-to-end shape information extraction and classification of areal settlements. And it overcomes the deficiency of setting parameters through experience in traditional methods.