Table of Content

20 January 2021, Volume 50 Issue 1

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Review

Generalized photogrammetry of spaceborne, airborne and terrestrial multi-source remote sensing datasets

ZHANG Yongjun, ZHANG Zuxun, GONG Jianya

2021, 50(1):  1-11.  doi:10.11947/j.AGCS.2021.20200245

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Since the 21st century, with the rapid development of cloud computing, big data, internet of things, machine learning and other information technology fields, human beings have entered a new era of artificial intelligence. The subject of photogrammetry has also followed the tide of the new round of scientific and technological revolution and developed rapidly into the brand-new generalized photogrammetry and entered the era of integrated intelligent photogrammetry. Its carrier platform, instruments and data processing theories as well as application fields have also changed significantly. The multi-sensor and multi-level integrated stereo observation technologies from spaceborne, airborne and terrestrial platforms have been greatly developed. In this paper, the novel concept of generalized photogrammetry is first put forward, and its subject connotation, development characteristics and some key technologies and applications are discussed in details. Under the brand-new generalized photogrammetry framework, data acquisition presents the characteristics of multi-angle imaging, multi-modal collaboration, multi-time integration, multi-scale linkage, while data processing presents the trends of multi-feature coupling, multi-control constraints, multi architecture processing, and multi-disciplinary intersection. The all-round development and intelligent service of the general photogrammetry still need to make greater breakthroughs in the aspects of spaceborne, airborne and terrestrial multi perspective or multi-modal image processing, intelligent information extraction and monitoring, combined 3D modeling with point cloud and image, autonomous control of unmanned system, visual inspection of intelligent manufacturing system, etc. Finally, new theories and technologies from real-time or quasi real-time intelligent geometric processing of multi-source remote sensing datasets to information extraction and intelligent service need to be established, which will make a well foundation to meet the new eara of intelligent surveying and mapping.

Geodesy and Navigation

Accuracy assessment method of geoid based on GNSS-leveling and gravity field error characteristics

ZHANG Chuanyin, MA Xu, ZHANG Lei, DING Jian

2021, 50(1):  12-17.  doi:10.11947/j.AGCS.2021.20200211

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The lack of an effective accuracy assessment method of geoid is a key problem in the modernization of height datum and its application. In this paper, according to the error characteristics of GNSS-leveling height anomaly and gravity field in frequency domain, the technical requirements for the fusion of GNSS-leveling and gravity height anomaly are studied. Furthermore, a method of error expression and accuracy evaluation of geoid is proposed. Through the example test and analysis, the main conclusions are as follows: ①The accuracy of the fused height anomaly should be expressed by the error curve of height anomaly difference varying with the distance. ②For accuracy assessment of quasigeoid, two error indicators and two error curves are recommended, namely, the error of gravity height anomaly difference, the internal error of the fused height anomaly, the error curve of the fused height anomaly difference and the error curve of GNSS-levelling height anomaly difference. ③When the distance between two terrestrial points is close to or less than the average distance between GNSS leveling points, GNSS-levelling height anomaly plays a major role in the contribution of the fused height anomaly. ④The accuracy of the fused height anomaly in large scale is mainly controlled by gravity height anomaly.

Android smartphone GNSS high-precision real-time dynamic positioning

GAO Chengfa, CHEN Bo, LIU Yongsheng

2021, 50(1):  18-26.  doi:10.11947/j.AGCS.2020.20200107

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Based on the quality and peculiarity of smartphone GNSS measurements, the existing methods of real-time dynamic PPP and RTK positioning are improved, and better smartphone GNSS positioning results obtained. Both positioning methods use a constant-acceleration dynamic single-frequency Kalman filter model with unfixed carrier phase integer ambiguity. Improvements include using smartphone carrier phase observation uncertainty for gross error processing, adopting the strategy of between-satellite single-difference to eliminate the influence of that the differences between smartphone pseudorange and carrier phase observations are not fixed, and modifying the value of smartphone measurements noise variance in the Kalman filtering process. Using one selected smartphone for experimental verification, the results show that its real-time dynamic PPP can reach a stable positioning state within 99 s, the horizontal and vertical RMS positioning error after stabilization are 1.21 and 2.79 m, respectively. RTK positioning can reach a stable state in 29 s, the horizontal and vertical RMS positioning error after stabilization are 0.73 and 0.78 m respectively. The experimental test results show that the current GNSS positioning module of smartphones could provide more accurate location services, and even has the potential for mapping operations in certain specific scenarios.

Centimeter precise orbit determination for the Swarm satellites via undifferenced kinematic method

ZHANG Bingbing, NIU Jiqiang, WANG Zhengtao, XU Feng, TIAN Kunjun

2021, 50(1):  27-36.  doi:10.11947/j.AGCS.2021.20190165

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Using dual-frequency satellite-borne GPS observations from May 24 to May 30, 2015, the linear combination of Melbourne-Wübbena and ionosphere-free linear combination, based on the basis of precise point positioning technology, the batch least squares estimation method is used to precisely determine the Swarm undifferenced kinematic orbits with different orbital attitudes. The orbit accuracy is assessed using three methods, which include satellite-borne GPS phase observation residuals analysis, comparison with ESA reduced-dynamic orbits and external evaluation with SLR measurements. The results indicate: ①Swarm satellite-borne GPS phase observation residual RMS is in the range of 6~7 mm level. ②Comparisons with reduced-dynamic orbits computed by European Space Agency (ESA), radial, along-track and cross-track orbit difference RMS are in the range of 2~4 cm level. ③Comparisons with kinematic orbits computed by ESA, Radial, along-track and cross-track orbit difference RMS are in the range of 1~2 cm level. ④The external validation with satellite laser ranging (SLR) measurement shows RMS errors are in the range of 3~4 cm level. Therefore, it is feasible to use the undifferenced kinematic orbit determination method and the orbit determination strategy provided in this paper to carry out the precise orbit determination of swarm series satellites. The orbit determination accuracy is centimeter level.

GPS time series inversion of the healing process of the middle segment of the Longmenshan fault after the 2008 Wenchuan earthquake

ZHAO Jing, ZHAN Wei, REN Jinwei, JIANG Zaisen, GU Tie, LIU Jie, NIU Anfu, YUAN Zhengyi

2021, 50(1):  37-51.  doi:10.11947/j.AGCS.2021.20200047

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The analysis of post-seismic fault healing process is important for deepening our understanding of seismogenic mechanisms, seismic faulting theory, and seismic cycles. In this study, by using time series of continuous GPS stations from 2010.30 to 2013.30 between the Wenchuan earthquake and the Lushan earthquake, the dynamic evolution of fault locking and slip deficit rate in the middle-southern segment of the Longmenshan fault zone were inverted and analyzed by the negative dislocation program of TDEFNODE, and the healing process of the middle segment and the background of major earthquakes of the southwest segment were discussed based on the inversion results. The results show that the locking fraction of the ruptured area around the epicenter of the Wenchuan earthquake gradually increased, which was basically in creeping state in 2010, and then in state of strongly locked in 2013. The locked area also gradually increased from the southwest of the Wenchuan earthquake epicenter to the epicenter, which indicates that this part of the fault has been healing rapidly. Most of the ruptured zone in the northeast of the epicenter is still in creeping state and the fault has not yet begun healing. The fault near and southwest of the epicenter of the Lushan earthquake has been in state of strongly locked, and the slip deficit rate of the completely locked area has been decreasing year by year, probably indicating that the healed region shares part of the compression effect of the Bayan Har block on the Sichuan Basin. The above results indicate that the healing process and activity characteristics of different segments of the Longmenshan ruptured fault are significantly different after earthquake. The southwest segment of the Longmenshan fault zone is in state of strongly locked and the background for a major earthquake has strengthened in the case of rapid accumulation of compressive elastic strain.

A cesium atomic clock frequency anomaly detection algorithm based on clock prediction and its performance analyses

WU Yiwei

2021, 50(1):  52-60.  doi:10.11947/j.AGCS.2020.20190322

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A cesium atomic clock frequency anomaly detection algorithm based on clock prediction is proposed. The theory is based on the analytical expression of clock prediction uncertainty. Prediction errors are used as the test statistics and a binary hypothesis test is adopted. The test statistics all follow standard normal distributions. By means of setting a false alarm probability (P_FA) value and acquiring the distribution function of normal distributions, the detection threshold can be determined. According to the detection threshold and the distribution function of the test statistics when anomalies appear, the expression of the detection probabilities (P_D)s with different mean frequency jump levels (Y_a)s is derived. Simulations and experiments validate the theoretical analyses. Based on the analyses, the methods of improving the detection probability are summarized.

Modeling and assessment of GPS/BDS/Galileo triple-frequency precise point positioning

ZHOU Feng, XU Tianhe

2021, 50(1):  61-70.  doi:10.11947/j.AGCS.2021.20200146

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With careful consideration of time-varying characteristics of pseudorange and carrier phase hardware biases, more rigorous undifferenced and uncombined observation equations are derived, and the mathematical expressions of the two types of GNSS biases in uncombined mode are given. Based on this, this contribution studied independent parameterization methods of three commonly used triple-frequency precise point positioning (PPP) (i.e., ionosphere-free combination IF1213 and IF123 as well as uncombination UC123) function models in detail, and systematically analyzed the relationship among these models. Subsequently, the positioning performance in terms of positioning accuracy for GPS/BDS/Galileo triple-frequency PPP in static and kinematic modes was assessed. The experimental results showed that the positioning accuracy after convergence for static PPP was better than 1.0 and 1.5 cm; while that for kinematic PPP was better than 2.0 and 5.0 cm in horizontal and vertical components, respectively. Moreover, the positioning performance of triple-frequency and dual-frequency PPP was comparable.

Seafloor classification based on combined multibeam bathymetry and backscatter using deep convolution neural network

YANG Fanlin, ZHU Zhengren, LI Jiabiao, FENG Chengkai, XING Zhe, WU Ziyin

2021, 50(1):  71-84.  doi:10.11947/j.AGCS.2021.20200065

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Seafloor classification is of great significance for the development and utilization of marine resources and marine scientific research. At present, multibeam detection is one of the effective methods to achieve large-scale seafloor classification. Seafloor classification is usually based on the angular response (AR) features and backscatter image features extracted by using multibeam backscatter. Because the feature source is relatively single and classifier structure is simple, the classification accuracy is often not high. This paper proposes a seafloor classification method based on convolutional neural networks (CNN). In addition to backscatter features, bathymetry features are also used to classify. The feature vectors are converted into waveform maps, and then input to the convolutional neural network for training and classification. The experiment compares different feature combination models and four conventional classifiers: BP network, support vector machine (SVM), k-nearest neighbor (KNN), and random forest (RF). The overall classification accuracy of the experiment reaches 94.86%, the kappa coefficient up to 0.93, and it takes 1 min 25 s. The accuracy has obvious advantages and the efficiency is relatively high. This method can effectively obtain the seafloor information in two different data types, give full play to the characteristics of convolutional neural network weight sharing, high efficiency, and achieve high-resolution seafloor classification. This paper provides a reference for the seafloor classification based on multibeam.

Photogrammetry and Remote Sensing

Block adjustment of satellite imagery with line-of-sight vector rectification

WU Yang, ZHANG Yongsheng, LI Kai, YU Ying, LAI Guangling

2021, 50(1):  85-96.  doi:10.11947/j.AGCS.2021.20190093

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Considering the traditional bundle block adjustment of rational function model is highly constrained by small error of attitude and orbit, narrow field camera and good image intersection angle, a block adjustment method of satellite imagery based on line-of-sight vector rectification was presented. Firstly, the light ray of an image point is calculated by using the rational polynomial coefficients attached to the image, and then the pseudo orbit and attitude of sensor is restored when the point was acquired, then an error compensation model is constructed for the virtual orbit and attitude, and finally the model parameters and object coordinates of tie points are solved simultaneously by the least square method. As the compensation model is modelled from the reason of system errors, it can avoid approximation assumptions and constraints of traditional strategy. Several comparative experiments of simulation data, mapping satellite and non-mapping satellite data are designed. The results show that this algorithm can achieve higher accuracy than the traditional method under various severe conditions such as images with large attitude angle error, large field angle and weak intersection angle.

Urban villages extraction from high-resolution remote sensing imagery based on landscape semantic metrics

ZHANG Tao, DING Lele, SHI Furong

2021, 50(1):  97-104.  doi:10.11947/j.AGCS.2021.20190463

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Urban villages (UVs), a special type of informal settlement in China. In this study, we proposed a method for UV extraction from high-resolution remote sensing imagery using landscape semantic metrics that can describe the complicated scene of UVs. In addition, an “uncertainty-feedback” strategy was proposed for large-scale practicable UV mapping. The experiment was performed in the urban areas of Guangzhou, with overall accuracy larger than 90%. The results reveal that the landscape semantic metrics have better ability to describe the essential characteristics of UVs compared to the traditional spectral and textural features. Besides, the “uncertainty-feedback” strategy can make full use of the classification reliability output by the machine learning, and produce more accurate UV mapping results with limited manual intervention. Thus, the proposed method can be effectively applied to large-scale UV extraction and mapping.

A divided and stratified extraction method of high-resolution remote sensing information for cropland in hilly and mountainous areas based on deep learning

LIU Wei, WU Zhifeng, LUO Jiancheng, SUN Yingwei, WU Tianjun, ZHOU Nan, HU Xiaodong, WANG Lingyu, ZHOU Zhongfa

2021, 50(1):  105-116.  doi:10.11947/j.AGCS.2021.20190448

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Cropland is a scarce land resource in hilly and mountainous areas, which has the characteristics of complex topographic conditions and diverse planting structures, leading to the difficulty of rapid and accurate acquisition of cropland information in mountainous areas. Therefore, it is difficult to extract the cropland information in mountainous areas quickly and automatically based on the traditional remote sensing data and remote sensing monitoring methods. Aiming at this problem, this paper takes Xifeng County of Guizhou Province in the southwest mountainous area as the experimental area. According to the heterogeneity of geospatial space, this paper proposes the idea of cropland morphological information extraction by geographical division control and stratification extraction, and constructs a method for extracting cropland morphological information based on geographical division control and stratification extraction under the constraints of geomorphic unit. Firstly, according to the geomorphology-vegetation characteristics, the experimental area is divided into three geographical zones: flatland, hillside area and forest. Then, on the basis of each type of partition, the cropland is divided into different types according to the visual characteristics presented by the cropland, and different deep learning models are designed for hierarchical extraction of different types of cropland. The experimental results show that this method has a good suppression effect on the background noise of complex terrain in mountainous areas, and the extracted cropland plot information is more consistent with the actual distribution pattern of the actual cropland compared with the traditional method, which effectively reduces the rate of missing extraction and wrong extraction.

Cartography and Geoinformation

Spatial relation reasoning and representation for image matching

LI Qin, YOU Xiong, LI Ke, WANG Weiqi

2021, 50(1):  117-131.  doi:10.11947/j.AGCS.2021.20190497

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The spatial relationship in the paper means the spatial adjacency relation between the objects in word space, and the paper proposes a novel image matching method based on analyzing the spatial adjacency relation between the object contained in the image pair. In the method, the feature extraction network is first trained based on comparison mechanism, and the well-trained model could produce the deep features for the objects, which could effectively match the consistent objects across images. Then the priori images are employed to deduce the spatial adjacency information between different objects, which is further represented by the spatial adjacency graph. Finally, the spatial relation matching between the image pair is conducted by calculating the spatial adjacency score based on the spatial adjacency graph. The experimental results demonstrate that the spatial adjacency scores of the non-matched image pairs generally equal to 0, and those of the matched pairs are generally greater than 0. As several objects are involved, the proposed spatial relation matching method could achieve high robustness, it outperforms other methods in the comparison experiments, and it could effectively complete the image matching task with high efficiency.

An improved detecting information model of point annotation labelling in cartography

HU Fengwei, QIAO Junjun, CHEN Zhangjian, GONG Lifang, LI Aiqin

2021, 50(1):  132-141.  doi:10.11947/j.AGCS.2021.20200018

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As one of the media of map information transmission, point annotation is an indispensable part of map. In the past, most of the researches on point annotation labelling automatically only considered confliction problem. However, they ignored the spatial distribution and structural characteristics of background features, which are closely related to point annotation quality. Therefore, we firstly construct a quantitative description of correlation between point annotation quality and background features by starting with the annotation's clarity, explicity, distribution uniformity and attribution correctness rules. Then we proposed an improved detecting information model which takes into account the detecting information caused by feature topological relation, data structure, visual position and district. We experimented the proposed method and compared with the first generation model and Maplex on national 1∶1 million geographic information data, results showed that the model could effectively decrease detecting information in local area and avoid background features maximally, then improve point annotation's clarity and Readability.

Summary of PhD Thesis

Research on features extraction and simplification from triangle mesh based on Morse theory

ZHANG Chunkang

2021, 50(1):  142-142.  doi:10.11947/j.AGCS.2021.20190491

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