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| 1 | Resilient PNT Concept Frame显示文摘The concept of resilient positioning, navigation and timing (PNT) is described. The definition of resilient PNT is given, the relationship between integrated (or comprehensive) PNT and resilient PNT is analyzed, and it is pointed out that the integrated PNT is the foundation of resilient PNT. Resilient PNT should be divided into resilient sensor integration, resilient functional model and resilient stochastic model. The strategy and principles of resilient integration of sensors are discussed. The resilient integration of sensors should be designed following the optimal, available, compatible and interoperable principles. The concepts of resilient functional model and possible modification strategies of the different functional models are also described. Several possible optimal routes for resilient stochastic model improvements are also set forth. It is pointed out that the optimal improvements of stochastic models for multi PNT sources should follow the same variance scale. At last, the resilient PNT data fusion for state parameters are given based on the resilient functional and stochastic models. | Yuanxi YANG | 2019 | Journal of Geodesy and Geoinformation Science2019，2，3： | 21 |
| 2 | Research on Key Technologies of Precise InSAR Surveying and Mapping Applications Using Automatic SAR Imaging显示文摘Precise interferometric synthetic aperture radar (InSAR) is a new intelligent photogrammetric technology that uses automatic imaging and processing means. Precise InSAR has become the most efficient satellite surveying and mapping (SASM) method that uses the interferometric phase to create a global digital elevation model (DEM) with high precision. In this paper, we propose the application of systematic InSAR technologies to SASM. Three key technologies are proposed: calibration technology, data processing technology and post-processing technology. First, we need to calibrate the geometric and interferometric parameters including the azimuth time delay, range time delay, and atmospheric delay, as well as baseline errors. Second, we use the calibrated parameters to create a precise DEM. One of the important procedures in data processing is the determination of phase ambiguities. Finally, we improve the DEM quality through the joint use of the block adjustment method, long and short baseline combination method and descending and ascending data merge method. We use 6 sets of TanDEM-X data covering Shanxi to conduct the experiment. The root mean square error of the final DEM is 5.07 m in the mountainous regions. In addition, the low coherence area is 0.8 km 2. The result meets the China domestic SASM accuracy standard at both the 1∶50 000 and 1∶25 000 measurement scales. | Xinming TANG Tao LI Xiaoming GAO Qianfu CHEN Xiang ZHANG | 2019 | Journal of Geodesy and Geoinformation Science2019，2，2： | 20 |
| 3 | A Road Extraction Method for Remote Sensing Image Based on Encoder-Decoder Network显示文摘According to the characteristics of the road features,an Encoder-Decoder deep semantic segmentation network is designed for the road extraction of remote sensing images.Firstly,as the features of the road target are rich in local details and simple in semantic features,an Encoder-Decoder network with shallow layers and high resolution is designed to improve the ability to represent detail information.Secondly,as the road area is a small proportion in remote sensing images,the cross-entropy loss function is improved,which solves the imbalance between positive and negative samples in the training process.Experiments on large road extraction datasets show that the proposed method gets the recall rate 83.9%,precision 82.5%and F1-score 82.9%,which can extract the road targets in remote sensing images completely and accurately.The Encoder-Decoder network designed in this paper performs well in the road extraction task and needs less artificial participation,so it has a good application prospect. | Hao HE Shuyang WANG Shicheng WANG Dongfang YANG Xing LIU | 2020 | Journal of Geodesy and Geoinformation Science2020，3，2： | 20 |
| 4 | Photogrammetry and Deep Learning显示文摘Deep learning has become popular and the mainstream technology in many researches related to learning,and has shown its impact on photogrammetry.According to the definition of photogrammetry,that is,a subject that researches shapes,locations,sizes,characteristics and inter-relationships of real objects from optical images,photogrammetry considers two aspects,geometry and semantics.From the two aspects,we review the history of deep learning and discuss its current applications on photogrammetry,and forecast the future development of photogrammetry.In geometry,the deep convolutional neural network(CNN)has been widely applied in stereo matching,SLAM and 3D reconstruction,and has made some effects but needs more improvement.In semantics,conventional methods that have to design empirical and handcrafted features have failed to extract the semantic information accurately and failed to produce types of“semantic thematic map”as 4D productions(DEM,DOM,DLG,DRG)of photogrammetry.This causes the semantic part of photogrammetry be ignored for a long time.The powerful generalization capacity,ability to fit any functions and stability under types of situations of deep leaning is making the automatic production of thematic maps possible.We review the achievements that have been obtained in road network extraction,building detection and crop classification,etc.,and forecast that producing high-accuracy semantic thematic maps directly from optical images will become reality and these maps will become a type of standard products of photogrammetry.At last,we introduce our two current researches related to geometry and semantics respectively.One is stereo matching of aerial images based on deep learning and transfer learning;the other is precise crop classification from satellite spatio-temporal images based on 3D CNN. | Jianya GONG Shunping JI | 2018 | Journal of Geodesy and Geoinformation Science2018，1，1： | 19 |
| 5 | Progresses and Prospects of Marine Geodetic Datum and Marine Navigation in China显示文摘Territorial water is a significant part of national sovereignty of China,thus the infrastructures of national space datum and location services should cover the sea areas except for the land areas.China has established relatively complete geodetic coordinate frame in land areas over the past decades,including the lastest developed China Geodetic Coordinate System 2000(CGCS 2000)with its reference frame and the national gravity datum 2000.However,the currently used geodetic infrastructures have not well covered the sea areas of China.The marine geodetic datum and marine navigation technologies need to be further developed and extended to satisfy the national demands of marine environment and resources detection,scientific investigation as well as marine economy development in new era of China.This paper mainly reviews the development and the progress of Chinese marine geodetic datum and marine navigation,analyses related key technologies in establishing our national marine geodetic datum.Some current trends and future directions for independently developing our national marine geodetic datum and marine navigation technologies are discussed. | Yuanxi YANG Tianhe XU Shuqiang XUE | 2018 | Journal of Geodesy and Geoinformation Science2018，1，1： | 16 |
| 6 | Satellite Image Matching Method Based on Deep Convolutional Neural Network显示文摘This article focuses on the first aspect of the album of deep learning: the deep convolutional method. The traditional matching point extraction algorithm typically uses manually designed feature descriptors and the shortest distance between them to match as the matching criterion. The matching result can easily fall into a local extreme value, which causes missing of the partial matching point. Targeting this problem, we introduce a two-channel deep convolutional neural network based on spatial scale convolution, which performs matching pattern learning between images to realize satellite image matching based on a deep convolutional neural network. The experimental results show that the method can extract the richer matching points in the case of heterogeneous, multi-temporal and multi-resolution satellite images, compared with the traditional matching method. In addition, the accuracy of the final matching results can be maintained at above 90%. | Dazhao FAN Yang DONG Yongsheng ZHANG | 2019 | Journal of Geodesy and Geoinformation Science2019，2，2： | 16 |
| 7 | Salient Object Detection from Multi-spectral Remote Sensing Images with Deep Residual Network显示文摘alient object detection aims at identifying the visually interesting object regions that are consistent with human perception. Multispectral remote sensing images provide rich radiometric information in revealing the physical properties of the observed objects, which leads to great potential to perform salient object detection for remote sensing images. Conventional salient object detection methods often employ handcrafted features to predict saliency by evaluating the pixel-wise or superpixel-wise contrast. With the recent use of deep learning framework, in particular, fully convolutional neural networks, there has been profound progress in visual saliency detection. However, this success has not been extended to multispectral remote sensing images, and existing multispectral salient object detection methods are still mainly based on handcrafted features, essentially due to the difficulties in image acquisition and labeling. In this paper, we propose a novel deep residual network based on a top-down model, which is trained in an end-to-end manner to tackle the above issues in multispectral salient object detection. Our model effectively exploits the saliency cues at different levels of the deep residual network. To overcome the limited availability of remote sensing images in training of our deep residual network, we also introduce a new spectral image reconstruction model that can generate multispectral images from RGB images. Our extensive experimental results using both multispectral and RGB salient object detection datasets demonstrate a significant performance improvement of more than 10% improvement compared with the state-of-the-art methods. | Yuchao DAI Jing ZHANG Mingyi HE Fatih PORIKLI Bowen LIU | 2019 | Journal of Geodesy and Geoinformation Science2019，2，2： | 14 |
| 8 | Applicability of Bevis Formula at Different Height Levels and Global Weighted Mean Temperature Model Based on Near-earth Atmospheric Temperature显示文摘Weighted mean temperature(T m)is a critical parameter in Global Navigation Satellite System(GNSS)technology to retrieve precipitable water vapor(PWV).It is convenient to obtain high-precision T m estimates near surface utilizing Bevis formula and surface temperature.However,some researches pointed out that the Bevis formula has large uncertainties in high-altitude regions.We investigate the applicability of the Bevis formula at different height levels and find that the Bevis formula has relatively high precision when the altitude is low,while with altitude increasing,the precision decreases gradually.To solve the problem,we analyze the relationship between T m and atmospheric temperature within the near-earth space range(the height range between 0~10 km)and find that they have a high correlation on a global scale.Accordingly,we build a global weighted mean temperature model based on near-earth atmospheric temperature.Validation results of the model show that this model can provide high-precision T m estimation at any height level in the near-earth space range. | Yibin YAO Zhangyu SUN Chaoqian XU | 2020 | Journal of Geodesy and Geoinformation Science2020，3，1： | 14 |
| 9 | Object Detection Research of SAR Image Using Improved Faster Region-Based Convolutional Neural Network显示文摘Target detection technology of synthetic aperture radar(SAR)imageis widely used in the field of military reconnaissance and surveillance.The traditional SAR image target detection methods need to be provided a lot of empirical knowledge because the characteristics of SAR images in different configurations(attitude,pitch angle,imaging parameters,etc.)will change greatly,resulting in high generalization error.Currently,deep learning method has achieved great success in the field of image processing.Research shows that deep learning can achieve a more intrinsic description of the data,while the model has a stronger ability of modeling and generalization.In order to solve the problem of insufficient data in SAR data sets,an experimental system for acquiring SAR image data in real scenes was built.Then the transfer learning method and the improved convolution neural network algorithm(PCA+Faster R-CNN)are applied to improve the target detection precision.Finally,experimental results demonstrate the significant effectiveness of the proposed method. | Long SUN Tao WU Guangcai SUN Dazheng FENG Lieshu TONG Mengdao XING | 2020 | Journal of Geodesy and Geoinformation Science2020，3，3： | 13 |
| 10 | Land Surface Displacement Geohazards Monitoring Using Multi-temporal InSAR Techniques显示文摘China has been affected by some of the world’s most serious geological disasters and experiences high economic damage every year.Geohazards occur not only in remote areas but also in highly populated cities.In the framework of the Dragon-432365 Project,this paper presents the main results and the major conclusions derived from an extensive exploitation of Sentinel-1,ALOS-2(Advanced Land Observing Satellite 2),GF-3(Gao Fen Satellite 3),and latest launched SAR(Synthetic Aperture Radar),together with methods that allow the evaluation of their importance for various geohazards.Therefore,in the scope of this project,the great benefits of recent remote sensing data(wide spatial and temporal coverage)that allow a detailed reconstruction of past displacement events and to monitor currently occurring phenomena are exploited to study different areas and geohazards problems,including:surface deformation of mountain slopes;identification and monitoring of ground movements and subsidence;landslides;ground fissure;and building inclination studies.Suspicious movements detected in the different study areas were cross validated with different SAR sensors and truth data. | Guang LIU Perski ZBIGNIEW Salvi STEFANO Thiebes BENNI Lixin WU Jinghui FAN Shibiao BAI Lianhuan WEI Shiyong YAN Rui SONG Bignami CHRISTIAN Tolomei CRISTIANO Stefan SCHNEIDERBAUER Joao Sousa JOAQUIM 无 | 2021 | Journal of Geodesy and Geoinformation Science2021，4，1： | 13 |
| 11 | Complex Least Squares Adjustment to Improve Tree Height Inversion Problem in PolInSAR显示文摘At present,the principal data processing methods involving complex observations are based on two strategies according to characteristics of the observation process,i.e.,step-by-step and direct resolution.However,these strategies have some limitations,e.g.they cannot consider statistical observation error information,redundant observations and so on.This paper applies least squares methods to complex data processing to extend surveying adjustment theory from real to complex number space.We compared the two adjustment criteria for a complex domain in a quantitative way.In order to understand the effectiveness of complex least squares,tree height inversion from PolInSAR data is taken as an example.We firstly established both a complex adjustment function model and a stochastic model for PolInSAR tree height inversion,and then applied the complex least squares method to estimate tree height.Results show that the complex least squares approach is reliable and outperforms other classic tree height retrieval methods;the method is simple and easy to implement. | Jianjun ZHU Qinghua XIE Tingying ZUO Changcheng WANG Jian XIE | 2019 | Journal of Geodesy and Geoinformation Science2019，2，1： | 12 |
| 12 | Information Theory of Cartography:An Information-theoretic Framework for Cartographic Communication显示文摘Map is one of the communication means created by human being.Cartographers have been making efforts on the comparison of maps to natural languages so as to establish a'cartographic language'or'map language'.One of such efforts is to adopt the Shannon’s Information Theory originated in digital communication into cartography so as to establish an entropy-based cartographic communication theory.However,success has been very limited although research work had started as early as the mid-1960 s.It is then found that the bottleneck problem was the lack of appropriate measures for the spatial(configurational)information of(graphic and image)maps,as the classic Shannon entropy is only capable of characterizing statistical information but fails to capture the configurational information of(graphic and image)maps.Fortunately,after over 40-year development,some bottleneck problems have been solved.More precisely,generalized Shannon entropies for metric and thematic information of(graphic)maps have been developed and the first feasible solution for computing the Boltzmann entropy of image maps has been invented,which is capable of measuring the spatial information of not only numerical images but also categorical maps.With such progress,it is now feasible to build the'Information Theory of Cartography'.In this paper,a framework for such a theory is proposed and some key issues are identified.For these issues,some have already been tackled while others still need efforts.As a result,a research agenda is set for future action.After all these issues are tackled,the theory will become matured so as to become a theoretic basis of cartography.It is expected that the Information Theory of Cartography will play an increasingly important role in the discipline of cartography because more and more researchers have advocated that information is more fundamental than matter and energy. | Zhilin LI Peichao GAO Zhu XU | 2021 | Journal of Geodesy and Geoinformation Science2021，4，1： | 11 |
| 13 | A Deep Double-Channel Dense Network for Hyperspectral Image Classifica-tion显示文摘Hyperspectral Image(HSI)classification based on deep learning has been an attractive area in recent years.However,as a kind of data-driven algorithm,the deep learning method usually requires numerous computational resources and high-quality labelled datasets,while the expenditures of high-performance computing and data annotation are expensive.In this paper,to reduce the dependence on massive calculation and labelled samples,we propose a deep Double-Channel dense network(DDCD)for Hyperspectral Image Classification.Specifically,we design a 3D Double-Channel dense layer to capture the local and global features of the input.And we propose a Linear Attention Mechanism that is approximate to dot-product attention with much less memory and computational costs.The number of parameters and the consumptions of calculation are observably less than contrapositive deep learning methods,which means DDCD owns simpler architecture and higher efficiency.A series of quantitative experiences on 6 widely used hyperspectral datasets show that the proposed DDCD obtains state-of-the-art performance,even though when the absence of labelled samples is severe. | Kexian WANG Shunyi ZHENG Rui LI Li GUI | 2021 | Journal of Geodesy and Geoinformation Science2021，4，4： | 11 |
| 14 | Data Logic Structure and Key Technologies on Intelligent High-precision Map显示文摘Taking autonomous driving and driverless as the research object,we discuss and define intelligent high-precision map.Intelligent high-precision map is considered as a key link of future travel,a carrier of real-time perception of traffic resources in the entire space-time range,and the criterion for the operation and control of the whole process of the vehicle.As a new form of map,it has distinctive features in terms of cartography theory and application requirements compared with traditional navigation electronic maps.Thus,it is necessary to analyze and discuss its key features and problems to promote the development of research and application of intelligent high-precision map.Accordingly,we propose an information transmission model based on the cartography theory and combine the wheeled robot’s control flow in practical application.Next,we put forward the data logic structure of intelligent high-precision map,and analyze its application in autonomous driving.Then,we summarize the computing mode of“Crowdsourcing+Edge-Cloud Collaborative Computing”,and carry out key technical analysis on how to improve the quality of crowdsourced data.We also analyze the effective application scenarios of intelligent high-precision map in the future.Finally,we present some thoughts and suggestions for the future development of this field. | Jingnan LIU Jiao ZHAN Chi GUO Tingting LEI Ying LI | 2020 | Journal of Geodesy and Geoinformation Science2020，3，3： | 11 |
| 15 | Progress and Applications of Visual SLAM显示文摘Visual simultaneous localization and mapping (SLAM) provides mapping and self-localization results for a robot in an unknown environment based on visual sensors, that have the advantages of small volume, low power consumption, and richness of information acquisition. Visual SLAM is essential and plays a significant role in supporting automated and intelligent applications of robots. This paper presents the key techniques of visual SLAM, summarizes the current research status, and analyses the new trends of visual SLAM research and development. Finally, specific applications of visual SLAM in restricted environments, including deep space and indoor scenarios, are discussed. | Kaichang DI Wenhui WAN Hongying ZHAO Zhaoqin LIU Runzhi WANG Feizhou ZHANG | 2019 | Journal of Geodesy and Geoinformation Science2019，2，2： | 11 |
| 16 | A Two-step Estimation Method of Troposphere Delay with Consideration of Mapping Function Errors显示文摘Mapping function errors are usually not taken into consideration, when space geodetic data observed by VLBI, GNSS and some other techniques are utilized to estimate troposphere delay, which could, however, probably bring non-ignorable errors to solutions. After analyzing the variation of mapping function errors with elevation angles based on several-year meteorological data, this paper constructed a model of this error and then proposed a two-step estimation method of troposphere delay with consideration of mapping function errors. The experimental results indicate that the method put forward by this paper could reduce the slant path delay residuals efficiently and improve the estimation accuracy of wet tropospheric delay to some extent. | Haopeng FAN Zhongmiao SUN Liping ZHANG Xiaogang LIU | 2020 | Journal of Geodesy and Geoinformation Science2020，3，1： | 11 |
| 17 | A Robust Fault Detection Algorithm for the GNSS/INS Integrated Navigation Systems显示文摘Integrity is an important index for GNSS-based navigation and positioning, and the receiver autonomous integrity monitoring (RAIM) algorithm has been presented for integrity applications. In the integrated navigation systems of a global navigation satellite system (GNSS) and inertial navigation system (INS),the conventional RAIM algorithm has been developed to extended receiver autonomous integrity monitoring (ERAIM). However, the ERAIM algorithm may fail and a false alarm may generate once the measurements are contaminated by significant outliers, and this problem is rarely discussed in the existing literatures. In this paper, a robust fault detection and the corresponding data processing algorithm are proposed based on the ERAIM algorithm and the robust estimation. In the proposed algorithm, weights of the measurements are adjusted with the equivalent weight function, and the efficiency of the outlier detection and identification is improved, therefore, the estimates become more reliable, and the probability of the false alarm is decreased. Experiments with the data collected under actual environments are implemented, and results indicate that the proposed algorithm is more efficient than the conventional ERAIM algorithm for multiple outliers and a better filtering performance is achieved. | Chen JIANG Shubi ZHANG Yizhi CAO Hui LI Hui ZHENG | 2020 | Journal of Geodesy and Geoinformation Science2020，3，1： | 10 |
| 18 | A Remote Sensing Image Semantic Segmentation Method by Combining Deformable Convolution with Conditional Random Fields显示文摘Currently,deep convolutional neural networks have made great progress in the field of semantic segmentation.Because of the fixed convolution kernel geometry,standard convolution neural networks have been limited the ability to simulate geometric transformations.Therefore,a deformable convolution is introduced to enhance the adaptability of convolutional networks to spatial transformation.Considering that the deep convolutional neural networks cannot adequately segment the local objects at the output layer due to using the pooling layers in neural network architecture.To overcome this shortcoming,the rough prediction segmentation results of the neural network output layer will be processed by fully connected conditional random fields to improve the ability of image segmentation.The proposed method can easily be trained by end-to-end using standard backpropagation algorithms.Finally,the proposed method is tested on the ISPRS dataset.The results show that the proposed method can effectively overcome the influence of the complex structure of the segmentation object and obtain state-of-the-art accuracy on the ISPRS Vaihingen 2D semantic labeling dataset. | Zongcheng ZUO Wen ZHANG Dongying ZHANG | 2020 | Journal of Geodesy and Geoinformation Science2020，3，3： | 10 |
| 19 | High-resolution Remote Sensing Image Segmentation Using Minimum Spanning Tree Tessellation and RHMRF-FCM Algorithm显示文摘It is proposed a high resolution remote sensing image segmentation method which combines static minimum spanning tree(MST)tessellation considering shape information and the RHMRF-FCM algorithm.It solves the problems in the traditional pixel-based HMRF-FCM algorithm in which poor noise resistance and low precision segmentation in a complex boundary exist.By using the MST model and shape information,the object boundary and geometrical noise can be expressed and reduced respectively.Firstly,the static MST tessellation is employed for dividing the image domain into some sub-regions corresponding to the components of homogeneous regions needed to be segmented.Secondly,based on the tessellation results,the RHMRF model is built,and regulation terms considering the KL information and the information entropy are introduced into the FCM objective function.Finally,the partial differential method and Lagrange function are employed to calculate the parameters of the fuzzy objective function for obtaining the global optimal segmentation results.To verify the robustness and effectiveness of the proposed algorithm,the experiments are carried out with WorldView-3(WV-3)high resolution image.The results from proposed method with different parameters and comparing methods(multi-resolution method and watershed segmentation method in eCognition software)are analyzed qualitatively and quantitatively. | Wenjie LIN Yu LI Quanhua ZHAO | 2020 | Journal of Geodesy and Geoinformation Science2020，3，1： | 10 |
| 20 | A Fast Aerial Image Matching Method Using Airborne LiDAR Point Cloud and POS Data显示文摘A novel tie point matching algorithm of aerial images with the assistance of airborne LiDAR point clouds and POS data is proposed Firstly,the conjugate point searching strategy used in traditional correlation coefficient matching is improved and a fast algorithm is presented.Secondly,an automatic camera boresight misalignment calibration method based on virtual ground control points is proposed,and then the searching range of image matching is adaptively determined and applied to the image matching of the entire surveying area.Test results verified that the fast correlation coefficient matching algorithm proposed in this paper can reduce approximately 25% of the matching time without the loss of matching accuracy.The camera boresight misalignment calibration method can effectively increase the accuracy of exterior orientation elements of images calculated from POS data,and thus can significantly improve the predicted position of conjugate point for tie point matching.Our proposed image matching algorithm can achieve superior matching accuracy with multi-scale,multi-view,and cross-flight aerial images. | Yongjun ZHANG Xiaodong XIONG Mengqiu WANG Yihui LU | 2019 | Journal of Geodesy and Geoinformation Science2019，2，1： | 10 |
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