Acta Geodaetica et Cartographica Sinica

Dyadic wavelet transform and signal extraction of GNSS coordinate time series with missing data

JI Kunpu, SHEN Yunzhong

2020, 49(5): 537-546. doi:10.11947/j.AGCS.2020.20190163

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The GNSS position time series are often analyzed by using traditional dyadic wavelet transform, which requires that the time series must be complete. However, missing data inevitably occur in the GNSS position time series due to a variety of causes. In order to extract signals from the incomplete position time series, a modified dyadic wavelet transform algorithm is developed and the corresponding formulas are derived in this paper based on the principle that missing data can be reproduced by its wavelet coefficients. The equivalence between new algorithm and zero-padding algorithm is proved, which indicates that the zero-padding algorithm is essentially a least squares minimum norm solution. Finally, the real position time series of 27 based stations from Crustal Movement Observation Network of China (CMONOC) and simulated data are adopted to verify the validation of the new algorithm, the results show that the difference between the signals extracted by new algorithm and interpolation algorithm is small, with the differences of mean medium errors of 27 base stations are only 2.01%(North), 0.54%(East), 1.26%(Up) and the mean ratios of variance for difference of two signals to the variance for two signals are only 1.16%(North), 0.54%(East), 1.62%(Up).

Subglacial lake activity and hydrological connection in the Byrd glacier basin during 2003—2018: analysis of multimission satellite altimetry monitoring results

CHEN Junlin, ZHOU Chunxia, ZHAO Qiuyang

2020, 49(5): 547-556. doi:10.11947/j.AGCS.2020.20190209

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The movement of water beneath the Antarctic ice sheet and the activity of subglacial lakes have important effects on regional ice dynamics, grounding-line stability, and ice sheet mass balance. It is particularly important to monitor activities of subglacial lakes. Combining the ICESat and CryoSat-2 altimeter datasets, the repeat orbit method and differential DEM method were used to monitor the activity of 17 glacial lakes in the Byrd glacier basin for 16 years in this paper. In addition, the variation of average elevation and average water amount of each subglacial lake is calculated, and the hydrological characteristics of subglacial lake activity are summarized. We used the water potential equation to obtain the drainage pathways map in Byrd glacier basin and combined subglacial lake activity to analyze the hydrological relationship among them. Byrd₁ subglacial lake and Byrd₂ subglacial lake both have the periodic pattern of water storage and drainage activities about 2~3 years. Byrd₁ subglacial lake are mainly affected by the activity of upstream Byrd₂ subglacial lake. The water volume of Byrd_s9 subglacial lake and Byrd_s14 subglacial lake kept rising because of supplementary by upstream Byrd_s11 and Byrd_s15 drainage respectively.

A multi-GNSS water vapor tomography method considering boundary incident signals

HU Peng, HUANG Guanwen, ZHANG Qin, YAN Xingyuan, LI Zhe

2020, 49(5): 557-568. doi:10.11947/j.AGCS.2020.20190113

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Three-dimensional water vapor distribution information with high spatial and temporal sampling can be obtained all-weather by using GNSS water vapor tomography technology, which has become a major research hotspot in GNSS meteorology. At present, the traditional tomography method based on single navigation system has some shortcomings, such as low utilization rate of observation data and uneven distribution of observation. This paper designs and implements a multi-GNSS water vapor tomography method which takes into account the boundary incident signal. The accuracy of the method is verified by using the observation data of Hong Kong CORS network and radio sounding products. The improvement of the results of water vapor tomography by introducing boundary incident signal and multi-GNSS signal is analyzed in detail. The results show that compared with the traditional GPS tomography method, the proposed water vapor tomography method, which combines the advantages of boundary incident signal and multi-GNSS signal, can obtain more accurate and reliable three-dimensional water vapor information.

A novel track measurement system based on GNSS/INS and multisensor for high-speed railway

LI Qi, BAI Zhengdong, CHEN Bobo, GUO Jingjun, XIN Haohao, CHENG Yuhang, LI Qiong, WU Fei

2020, 49(5): 569-579. doi:10.11947/j.AGCS.2020.20190344

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High-speed railway tracks must maintain high smoothness, high stability and high reliability, which is closely related to the high speed, safe and smooth running of high-speed trains. High-speed railway track measurement at least involves control survey, route survey, and deformation survey. Conventional high-speed railway track measurement method has the weaknesses such as long measurement cycle, high maintenance cost, and low detection efficiency. To this end, a novel track measurement system based on global navigation satellite system (GNSS)/inertial navigation system (INS) and multisensor for high-speed railway is proposed, and corresponding track measurement system is developed. This paper systematically introduces the main components and the process procedures of the track measurement system, and it has been applied in the actual high-speed railway track fine adjustment engineering and the results show that the proposed solution realizes the integration of the track subgrade deformation monitoring and the track irregularity absolute measurement and relative measurement for high-speed railway with 2 mm lateral deviation accuracy and 2 mm vertical deviation accuracy of track, and 1 mm horizontal accuracy and 1.5 mm vertical accuracy of deformation points, which significantly improves the measurement efficiency.

T-S fuzzy neural network to predict satellite clock bias

WANG Xu, CHAI Hongzhou, WANG Chang

2020, 49(5): 580-588. doi:10.11947/j.AGCS.2020.20190156

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To find a high accuracy method for SCB(satellite clock bias) prediction based on characteristics of SCB data, this study proposes an T-S fuzzy neural network model based on the change rate method to predict SCB. the change rate of two SCB values of adjacent epoch is first calculated to obtain the corresponding change rate sequences. Then, modeling is performed based on the change rate sequence to predict the change rate value using the T-S fuzzy neural network model (TSFNN). Finally, the predicted sequences are recovered to the corresponding predicted SCB. The new model is compared with IGU-P products, quadratic polynomial (QP) model, and gray model (GM (1,1)) through the predicted sequences. The results show that the prediction precisions and stability of TSFNN model have been improved about 69.8% and 76.3% respectively after using the change rate method, and the accuracy is about 10 times higher than IGU-P products. the prediction effect of the proposed model is better than two common models. Therefore, the proposed model can achieve high accuracy prediction of SCB.

Superpixel segmentation method of high-resolution remote sensing image based on fuzzy clustering

HUANG Liang, YAO Bingxiu, CHEN Pengdi, YANG Xing, FU Bihuan

2020, 49(5): 589-597. doi:10.11947/j.AGCS.2020.20190135

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Traditional fuzzy C-means clustering (FCM) only considers the gray features of image in image segmentation, which results in unsatisfactory segmentation results when the algorithm is applied to high-resolution remote sensing image segmentation. In order to solve this problem, a new method of superpixel segmentation method of high-resolution remote sensing image based on fuzzy C-means clustering is proposed in this paper. Firstly, watershed transform algorithm is used to generate multiple superpixels, and then the similarity of spectrum features among superpixels are compared. Finally, these superpixels are merged by a FCM method combined with spectrum features. Four sets of remote sensing images of different scenes were selected in the experiment, and the experimental results were evaluated by combining qualitative and quantitative methods. The experimental results show that the method can effectively improve accuracy of the segmentation and achieve better visual effect of the segmentation.

A joint geometric calibration technique for GF-3 SAR image in wide area

DING Liujian, TAO Qiuxiang, LI Tao, CHEN Qianfu, CHEN Yang

2020, 49(5): 598-610. doi:10.11947/j.AGCS.2020.20190276

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In order to obtain the accurate SAR orthophoto over wide area, it is necessary to modify the geometric parameters for each of the SAR images. However, it is time consuming and laborious to correct each of the SAR images over wide area, which seriously decreased the efficiency of SAR image analysis. Therefore, a wide-area joint geometric calibration method is proposed in this paper. Firstly, the independent system-level calibration parameter is calculated using several SAR images on the basis of R-D model. Secondly, the influence of atmospheric delay component is removed from the system-level calibration parameters. Then, the optimal system-level calibration parameters are obtained based on the least squares adjustment principle. Finally, the other SAR images are corrected using the optimal system-level calibration parameters.In this paper, 32 scenes of GF-3 SAR images covering the central and eastern part of China are used for joint geometric calibration. The positioning error of the calibrated SAR images is less than 9 m. The experimental results show that the accuracy of geometric calibration for GF-3 SAR images can be improved by the proposed approach, and thus the effectiveness and feasibility of the proposed approach is demonstrated.

Feature-representation-transfer based road extraction method for cross-domain aerial images

WANG Shuyang, MU Xiaodong, HE Hao, YANG Dongfang, MA Chenhui

2020, 49(5): 611-621. doi:10.11947/j.AGCS.2020.20190274

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Aiming at the problem of the insufficient generalization ability of traditional road extraction methods when applying to a new dataset, this paper proposes a cross-domain road extraction method that realized by feature-representation-transfer and encoder-decoder network. Firstly, a basic road extraction model based on encoder-decoder network is designed to segment the road from a single data source. Then, based on the structure of road extraction network and the principle of cycle-consistent, a cycle generative adversarial network for feature transfer of cross-domain imagery is used, which maps the feature of target city images to the domain of source data. Finally, the pre-trained road extraction model is used to segment the target domain images after the feature transfer, so that the cross-domain road extraction can be realized. The experimental results show that the proposed method improves the generalization ability of the road extraction network and can extract the road target from cross-domain images accurately and effectively. Compared with the results without feature transfer, the proposed method greatly improves the road extraction metric, and increases the F1-score by more than 50%. The proposed method does not require any annotation of the target domain images, nor does it need to fine-tune the road extraction network, while it only need to train the feature transfer model from the target domain to the source domain. Therefore, it has good application value.

A method for recognizing building clusters by considering functional features of buildings

LIU Huimin, HU Wenke, TANG Jianbo, SHI Yan, DENG Min

2020, 49(5): 622-631. doi:10.11947/j.AGCS.2020.20190222

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From the perspective of space-semantic dual constraints, a method for building spatial distribution pattern recognition considering spatial proximity and functional similarity is proposed. Firstly, under the space-semantic divide and conquer strategy, buildings are clustering based on the spatial proximity of the building (i.e. the minimum distance between buildings), and the spatial distribution pattern of the building and the spatial proximity between the buildings are constructed. Then, the clusters are divided into partition according to the functional semantic similarity constraint of the building, the preliminary clustering results of the building are obtained. Finally, the overall clustering results are optimized based on intra-cluster similarity and inter-cluster differences. The experimental results show that the proposed method is able to recognize the building groups with spatial proximity and functional semantics similarity by comparison of the existing methods, and the recognition result is more in line with the need for semantic level generalization of buildings and research on urban structure in smart city applications.

A fast fusion object determination method for multi-path video and three-dimensional GIS scene

LIU Zhendong, DAI Zhaoxin, LI Chengming, LIU Xiaoli

2020, 49(5): 632-643. doi:10.11947/j.AGCS.2020.20190282

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Virtual and real fusion technology is one of the key links in video enhancement of 3D geographical scene, which plays an important role in improving the real immersive visual experience. The existing video projection fusion method usually needs to traverse all the rendered objects in the new scene when the 3D scene is updated dynamically, in order to determine the objects that need to be fused in the camera range, which resulting in a long fusion process and low efficiency. A fast fusion method of multi-channel video and 3D GIS scene considering topological relationship is proposed. Firstly, a dynamic topology network model was established for video objects and rendering objects to accelerate the judgment of spatial relationship. Secondly, the topology relationship between video and rendering objects within 3D scene was established based on the topology network model, and the recorded topological relationship is used to determine and fuse the object quickly. Finally, the three-dimensional scene of Tengzhou City is verified. The experimental results show that the proposed method has the advantages of less time-consuming, high efficiency, greatly reducing the computational cost of fusion, and has good feasibility and robustness in the fusion of video and 3D GIS scene.

Division of TIN_DDM topographic forms and continuous scale representation based on rolling ball transformation

ZHANG Zhiheng, DONG Jian, PENG Rencan, LU Yi, CHEN Qiu, ZHU Qiang

2020, 49(5): 644-655. doi:10.11947/j.AGCS.2020.20180527

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Aiming at the problems of special application scenarios, special data type, uncertain boundary of topographic forms and differences in topographic scale cognition which exist in the division of TIN_DDM topographic forms and continuous scale representation, the paper has brought forward the algorithm about division of TIN_DDM topographic forms and continuous scale representation based on rolling ball transformation. Based on the analysis of the principle of rolling ball transformation construction, by excavating the potential of rolling ball transformation in terrain recognition, the algorithm designs the criteria of determining the topographic type of TIN_DDM point. Aiming at the problem that the criteria can’t be applied in the continuous scale representation of topography, by analyzing the numerical correlation between rolling ball contact point and rolling ball radius, the critical rolling ball radius of the contact state between TIN_DDM point and rolling ball changing is calculated, the rolling ball transformation for the continuous scale representation of TIN_DDM topography is built. The experiments show that the algorithm can quickly realize the automatic division and continuous scale representation of topographic forms with rolling ball as scale factor.

A multi-resolution texture-based visualization method for geographic vector fields by means of spatial variation features

SHANG Daiyu, DING Yulin, ZHU Qing, WU Linbao

2020, 49(5): 656-666. doi:10.11947/j.AGCS.2020.20190160

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How to visualize massive, high-dimensional, dynamic and multi-scale geospatial vector fields online under the network environment, has been the key issue for data integration and comprehensive analysis of massive and intensive vector fields. Geographic vector fields contain various irregular and multi-scale variation features and structures with prominent spatio-temporal heterogeneity. Due to global uniform sampling density, there are typical problems of occlusion and low feature discrimination in traditional texture-based vector field visualization. To overcome these shortcomings, this paper proposes a multi-resolution texture-based visualization method for geographic vector fields by means of spatial variation features. The proposed method uses composite information entropy as the description of spatial variation features, so as to drive the generation of feature-constrained multi-resolution noise, construction and enhancement of multi-resolution texture of vector fields in image space, which can effectively overcome the technical bottleneck of the traditional texture visualization method. Experimental results of the wind flow dataset prove the reliability and effectiveness of the method.

Study on the information processing technology of infrared TDI scanning camera

PAN Shengda

2020, 49(5): 667-667. doi:10.11947/j.AGCS.2020.20190175

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Research on inter-frequency clock bias for GNSS multi-frequency integrated precise point positioning

PAN Lin

2020, 49(5): 668-668. doi:10.11947/j.AGCS.2020.20190195

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The research of key technology and approach for time and frequency transfer based on GNSS carrier phase observation

ZHANG Pengfei

2020, 49(5): 669-669. doi:10.11947/j.AGCS.2020.20190288

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Sea level change monitoring and prediction using satellite altimetry in South China Sea and East China Sea coastal ocean

ZHAO Jian

2020, 49(5): 670-670. doi:10.11947/j.AGCS.2020.20190302

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