Chinese Gravimetry Augment and Mass Change Exploring Mission Status and Future

The satellite gravimetry technology effectively recovers the global Earth’s gravity field.Since 2000s,HL-SST satellite CHAMP,LL-SST satellite GRACE,Gravity Gradient Measurement(GGM)satellite GOCE have been launched successfully,producing some Earth’s gravity models solely from satellites data.However,the space and time resolution of the Earth’s gravity fields do not adequately satisfy scientific objectives.The main reason is that the gravimetry satellites are not enough and observation data insufficient.The paper outlines the current and future status of Chinese gravity satellite missions.The Chinese gravimetry satellite system,named Chinese Gravimetry augment and Mass change exploring mission(ChiGaM),successfully launched in Dec.2021 after four years of production and over a year of calibration and valiation.The accelerometer,K-band ranging system and the three stellar sensors,among others,were precisely calibrated and trimmed.The satellite mass center was determined and coordinated with the proof center of accelerometer with an accuracy 100μm.The inter-satellite ranging system and BDS/GPS receiver operate together seamlessly.The range and range rate noise is less than 3μm/Hz^(1/2) and 1μm/s/Hz^(1/2),respectively,in band of 0.025~0.1 Hz.The electrostatic suspension accelerometer is working well.Its high-sensitive axis noise level is 3×10^(-10) m/s^(2)/Hz^(1/2)near the frequency 0.1 Hz,and 1×10^(-9) m/s^(2)/Hz^(1/2) for the less-sensitive axis.Meanwhile the BDS/GPS receiver is used to achieve data for precise orbit determination,yielding an orbit result with accuracy better than 2 cm.When compared with KBR observations,the RMS of the bias is less than 1 mm.Besides above mission,next gravimetric satellite is being developed now.TQ-2 mission is designed as a totally experimental satellite for gravitational wave detection at low Earth orbit,which can detect the Earth’s gravity field simultaneously.The Bender-type mission is considered the most promising configuration for TQ-2 and consists of two polar satellites and two inclined satellites.Due to the extra observations at the east-west direction derived from the inclined satellite pair,significant improvements has been made in detecting temporal signals with higher accuracy and spatial-temporal resolution.To achieve the scientific goal,the ACC MBW can shift from 0.001~0.1 Hz to 0.004~0.1 Hz for ACC,and the LRI MBW can shift from 0.01~1 Hz to 0.1~1 Hz.For future research,a gravimetric potential survey using cold-atomic-clock based on the general relativity theory,cold atom gradiometer should be pursued.Gravimetric technologies should be mined and researched,and the gravimetry satellite constellation should be developed,so as to improve the time resolution and space resolution for meeting the requirements of geophysics,geodesy,earthquake,water resources environment,oceanography,etc.

Grid Model for High-accuracy Coordinate Transformation of China Geodetic Coordinate System 2000

After implementing CGCS2000,establishing grid models for high-accuracy coordinate transformation which are mainly used to transform border lines and coordinate grids of topographic maps becomes an important issue in mapping applications.Consequently,a grid model for high-accuracy coordinate transformation of CGCS2000 is proposed.Specifically,we firstly analyze a minimum curvature equation of coordinate transformation,which possesses the characteristics of both the global and local smoothness,achieving better consistency with the consecutive smoothness for the coordinate transformation of map’s linear feature.Then an iterative calculation method of grid nodes and an approach for establishing regional grid models based on collocation by two-step minimization are proposed.Meanwhile,a data structure of grid model is constructed.Finally we give the optimized grid interval and transformation accuracy in China corresponding to the proposed grid model.Using 48 433 points of 2000 National Geodetic Control Network of China,we take the proposed model into practice by constructing grid models for coordinate transformation from BJS54 and XAS80 to CGCS2000,and the external positional accuracies for both models are 0.26 m and 0.03 m respectively.

Editorial Board

Editor-in-Chief Jianya GONG,Wuhan University,China Associate Editors-in-Chief Yuanxi YANG,Xi'an Research Institute of Surveying and Mapping,China Jiancheng LI,Wuhan University,China Zhilin II,The Hong Kong Polytechnic University,HongKong China Wenzhong SHI,The Hong Kong Polytechnic Unitersity,.HongKong China.

Marine Target Detection Using Dual-polarimetric SAR Imagery

In this study,we provide a summary of research advances in the field of maritime target detection using DP(dualpolarimetric)SAR(Synthetic Aperture Radar)imagery,accomplished during the European and China collaboration in the framework of the Dragon-4 program ID 32235.The main innovative contribution is twofold:(1)We addressed ship detection proposing an improved GP-PNF(Geometrical Perturbation-Polarimetric Notch Filter),termed as IGP-PNF,that is characterized by a new feature vector that includes three new scattering features;(2)We addressed oil platform detection by contrasting singlepolarization SAR methods with polarimetric ones in order to quantify the extra-benefit carried on polarimetric information.The proposed theoretical framework is tested against actual multi-polarization SAR data.In particular,ship detection methods are verified against a Sentinel-1 SAR scene where a large number of ships is present;while,oil platform detection is discussed using Terra SAR-X SAR data.Experimental analysis shows that:(1)The IGP-PNF method performs best in terms of clutter-to-target ratio;(2)Coherent polarimetric information significantly outperforms single-polarization SAR measurements in highlighting targets in challenging cases.

Disordered Multi-view Registration Method Based on the Soft Trimmed Deep Network

Compared with the pair-wise registration of point clouds,multi-view point cloud registration is much less studied.In this dissertation,a disordered multi-view point cloud registration method based on the soft trimmed deep network is proposed.In this method,firstly,the expression ability of feature extraction module is improved and the registration accuracy is increased by enhancing feature extraction network with the point pair feature.Secondly,neighborhood and angle similarities are used to measure the consistency of candidate points to surrounding neighborhoods.By combining distance consistency and high dimensional feature consistency,our network introduces the confidence estimation module of registration,so the point cloud trimmed problem can be converted to candidate for the degree of confidence estimation problem,achieving the pair-wise registration of partially overlapping point clouds.Thirdly,the results from pair-wise registration are fed into the model fusion to achieve the rough registration of multi-view point clouds.Finally,the hierarchical clustering is used to iteratively optimize the clustering center model by gradually increasing the number of clustering categories and performing clustering and registration alternately.This method achieves rough point cloud registration quickly in the early stage,improves the accuracy of multi-view point cloud registration in the later stage,and makes full use of global information to achieve robust and accurate multi-view registration without initial value.

A Road Extraction Method for Remote Sensing Image Based on Encoder-Decoder Network

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.

Correg-Yolov3:a Method for Dense Buildings Detection in High-resolution Remote Sensing Images

The exploration of building detection plays an important role in urban planning,smart city and military.Aiming at the problem of high overlapping ratio of detection frames for dense building detection in high resolution remote sensing images,we present an effective YOLOv3 framework,corner regression-based YOLOv3(Correg-YOLOv3),to localize dense building accurately.This improved YOLOv3 algorithm establishes a vertex regression mechanism and an additional loss item about building vertex offsets relative to the center point of bounding box.By extending output dimensions,the trained model is able to output the rectangular bounding boxes and the building vertices meanwhile.Finally,we evaluate the performance of the Correg-YOLOv3 on our self-produced data set and provide a comparative analysis qualitatively and quantitatively.The experimental results achieve high performance in precision(96.45%),recall rate(95.75%),F1 score(96.10%)and average precision(98.05%),which were 2.73%,5.4%,4.1%and 4.73%higher than that of YOLOv3.Therefore,our proposed algorithm effectively tackles the problem of dense building detection in high resolution images.

Object Detection Research of SAR Image Using Improved Faster Region-Based Convolutional Neural Network

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.

Network SpaceTime AI:Concepts,Methods and Applications

SpacetimeAI and GeoAI are currently hot topics,applying the latest algorithms in computer science,such as deep learning,to spatiotemporal data.Although deep learning algorithms have been successfully applied to raster data due to their natural applicability to image processing,their applications in other spatial and space-time data types are still immature.This paper sets up the proposition of using a network(&graph)-based framework as a generic spatial structure to present space-time processes that are usually represented by the points,polylines,and polygons.We illustrate network and graph-based SpaceTimeAI,from graph-based deep learning for prediction,to space-time clustering and optimisation.These applications demonstrate the advantages of network(graph)-based SpacetimeAI in the fields of transport&mobility,crime&policing,and public health.

A Review of RGB-D Camera Calibration Methods

RGB-D camera is a new type of sensor,which can obtain the depth and texture information in an unknown 3D scene simultaneously,and they have been applied in various fields widely.In fact,when implementing such kinds of applications using RGB-D camera,it is necessary to calibrate it first.To the best of our knowledge,at present,there is no existing a systemic summary related to RGB-D camera calibration methods.Therefore,a systemic review of RGB-D camera calibration is concluded as follows.Firstly,the mechanism of obtained measurement and the related principle of RGB-D camera calibration methods are presented.Subsequently,as some specific applications need to fuse depth and color information,the calibration methods of relative pose between depth camera and RGB camera are introduced in Section 2.Then the depth correction models within RGB-D cameras are summarized and compared respectively in Section 3.Thirdly,considering that the angle of the view field of RGB-D camera is smaller and limited to some specific applications,we discuss the calibration models of relative pose among multiple RGB-D cameras in Section 4.At last,the direction and trend of RGB-D camera calibration are prospected and concluded.

Estimating the Forest Above-ground Biomass Based on Extracted LiDAR Metrics and Predicted Diameter at Breast Height

Reliable and prompt information on forest above-ground biomass(AGB)and tree diameter at breast height(DBH)are crucial for sustainable forest management.Remote sensing technology,especially the Light Detection and Ranging(LiDAR)technology,has been proven to estimate important tree variables effectively.This study proposes predicting DBH and AGB from tree height and other LiDAR data extracted metrics.In the suggested DBH prediction,we developed a nonlinear estimation equation using the total tree height.As for the AGB prediction approach,we used regression methods such as multiple linear regression(MLR),random forest(RF)and support vector machine for regression(SVR).We conducted the study for the Gudao forest area dominated by Robinia Pseudoacacia trees,located in the Yellow River Delta(YRD),China.For our developed approaches,we used Unmanned Aerial Vehicle(UAV)and Backpack LiDAR point cloud datasets obtained in June 2017,and three field data measurements gathered in June 2017 and 2019 and October 2019,all from the same study area.The results demonstrate that:①The LiDAR data individual tree segmentation(ITS)from which we extracted individual tree information like tree location and tree height,was carried out with an overall accuracy F=0.91;②We used the ITS height data from the field stand in 2019 as a fit and developed a nonlinear DBH estimation equation with Root Mean Square Error(RMSE)=3.61 cm,later validated by the 2017 dataset;③Forest AGB at stand level was estimated with the MLR,RF and also SVR regression methods,and results show that the SVR method gave higher accuracy with R2=0.82 compared to the R2=0.72 of RF and the R2=0.70 of the MLR.Calculated AGB at plot level using the 2017 LiDAR data was used to validate both models’accuracy.Combining the UAV LiDAR data and the Backpack LiDAR significantly improved the overall ITS.The UAV LiDAR ability to provide high accuracy tree height abstraction,the DBH of the regression equation and other extracted LiDAR metrics showed high accuracy in estimating the forest AGB.This study shows that being cost-free is not the only advantage of free available software.In the performance of ITS and the LiDAR,metrics extraction proved to be as good as the commercially available software.

Parameter Group Optimization by Combining CUBE with Surface Filtering and Its Application

Shallow water multi-beam echo sounders(MBESs)are characterized by their high resolution and high density,and MBES data processing is a hotspot in modern marine surveying.The Combined Uncertainty and Bathymetry Estimator(CUBE)is the mainstream MBES data processing algorithm,although little is known about its core theories and parameters.In this paper,the basic principle,mathematical model,key parameters,and main processing steps of CUBE are described systematically.A parameter group optimization method that combines CUBE with a surface filter is established.Additionally,an example is given that shows the steps for parameter group optimization,including selection of a typical area,parameter group testing,and comparative analysis,and the method is then applied to shallow water MBES data processing.The results show that the method can improve the accuracy and efficiency of automatic data processing effectively,and it is thus of engineering application value.

A Remote Sensing Image Semantic Segmentation Method by Combining Deformable Convolution with Conditional Random Fields

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.

Progresses on SAR Remote Sensing of Tropical Forests:Forest Biomass Retrieval and Analysis of Changing Weather Conditions

This paper is intended to report on the progresses made during the Dragon-4 project Three and Four-Dimensional Topographic Measurement and Validation(ID:32278),sub-project Multi-baseline SAR Processing for 3 D/4 D Reconstruction(ID:322782).The work here reported focuses on two important aspects of SAR remote sensing of tropical forests,namely the retrieval of forest biomass and the assessment of effects due to changing weather conditions.Recent studies have shown that by using SAR tomography the backscattered power at 30 m layer above the ground is linearly correlated to the forest Above Ground Biomass(AGB).However,the two parameters that determine this linear relationship might vary for different tropical forest sites.For purpose of solving this problem,we investigate the possibility of using Li DAR derived AGB to help training the two parameters.Experimental results obtained by processing data from the Tropi SAR campaign support the feasibility of the proposed concept.This analysis is complemented by an assessment of the impact of changing weather conditions on tomographic imaging,for which we simulate BIOMASS repeat pass tomography using ground-based Tropi SCAT data with a revisit time of 3 days and rainy days included.The resulting backscattered power variation at 30 m is within 1.5 d B.For this forest site,this error is translated into an AGB error of about 50~80 t/hm^(2),which is 20%or less of forest AGB.

Near-zone Direct and Indirect Topographic Effects Based on the Rectangular Prism and Surface Element

Helmert’s second method of condensation is an effective method for terrain reduction in the geoid and quasi-geoid determinations. Condensing the masses outside the geoid to a surface layer on the geoid produces several forms of topographic effects: direct effect on gravity, secondary indirect effect on gravity and indirect effects on the (quasi-) geoid, respectively. To strike a balance between computation accuracy and numerical efficiency, the global integration region of topographic effects is usually divided into near zone and far zone. We focus on the computation of near-zone topographic effects, which are functions of actual topographic masses and condensed masses. Since there have already been mature formulas for gravitational attraction and potential of actual topographic masses using rectangular prism model, we put forward surface element model for condensed masses. Afterwards, the formulas for near-zone direct and indirect effects are obtained easily by combining the rectangular prism model and surface element model. To overcome the planar approximation errors involved with the new formulas for near-zone topographic effects, the Earth’s curvature can be taken into account. It is recommended to apply the formulas based on the rectangular prism and surface element considering the Earth’s curvature to calculate near-zone topographic effects for high-accuracy demand to determine geoid and quasi-geoid.

A Fast Aerial Image Matching Method Using Airborne LiDAR Point Cloud and POS Data

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.

Remote Sensing and Forest Carbon Monitoring——a Review of Recent Progress,Challenges and Opportunities

Remote sensing provides key inputs to a wide range of models and methods developed for quantifying forest carbon.In particular,carbon inventory methods recommended by IPCC require biomass data and a suite of forest disturbance products.Significant progress has been made in deriving these products by leveraging publicly available remote sensing assets,including observations acquired by the legendary Landsat mission and new systems launched within the past decade,including Sentinel-2,Sentinel-1,GEDI,and ICESAT-2.With the L-band NISAR and P-band BIOMASS missions to be launched in 2023,the Earth’s land surfaces will be imaged by optical and multi-band(including C-,L-,and P-bands)radar systems that can provide global,sub-weekly observations at sub-hectare spatial resolutions for public use.Fine scale products derived from these observations will be crucial for developing monitoring,reporting,and verification(MRV)capabilities needed to support carbon trade,REDD+,and other market-driven tools aimed at achieving climate mitigation goals through forest management at all levels.Following a brief discussion of the roles of forests in the global carbon cycle and the wide range of models and methods available for evaluating forest carbon dynamics,this paper provides an overview of recent progress and forthcoming opportunities in using remote sensing to map forest structure and biomass,detect forest disturbances,determine disturbance attribution,quantify disturbance intensity,and estimate harvested timber volume.Advances in these research areas require large quantities of well—distributed reference data to calibrate remote sensing algorithms and to validate the derived products.In addition,two of the forest carbon pools-dead organic matter and soil carbon—are difficult to monitor using modern remote sensing capabilities.Carefully designed inventory programs are needed to collect the required reference data as well as the data needed to estimate dead organic matter and soil carbon.

Three Dimensional Laser Point Cloud Slicing Method for Calculating Irregular Volume

Volume parameter is the basic content of a spatial body object morphology analysis.However,the challenge lies in the volume calculation of irregular objects.The point cloud slicing method proposed in this study effectively works in calculating the volume of the point cloud of the spatial object obtained through three-dimensional laser scanning(3DLS).In this method,a uniformly spaced sequent slicing process is first conducted in a specific direction on the point cloud of the spatial object obtained through 3DLS.A series of discrete point cloud slices corresponding to the point cloud bodies are then obtained.Subsequently,the outline boundary polygon of the point cloud slicing is searched one by one in accordance with the slicing sequence and areas of the polygon.The point cloud slice is also calculated.Finally,the individual point cloud section volume is calculated through the slicing areas and the adjacent slicing gap.Thus,the total volume of the scanned spatial object can be calculated by summing up the individual volumes.According to the results and analysis of the calculated examples,the slice-based volume-calculating method for the point cloud of irregular objects obtained through 3DLS is correct,concise in process,reliable in results,efficient in calculation methods,and controllable on accuracy.This method comes as a good solution to the volume calculation of irregular objects.

Orbital Elements Ephemerides and Interfaces Design of LEO Satellites

Low earth orbit satellites,with unique advantages,are prosperous types of navigation augmentation satellites for the GNSS satellites constellations.The broadcast ephemeris element needs to be designed as an important index of the augmented LEOs.The GPS ephemerides of 16/18 elements cannot be directly applied to the LEOs because of the poor fitting accuracies in along-track positional component.Besides,the ill-conditioned problem of the normal-matrix exists in fitting algorithm due to the small eccentricity of the LEO orbits.Based on the nonsingular orbital elements,5 sets of ephemerides with element numbers from 16 to 19 were designed respectively by adding or modifying orbital elements magnifying the along-track and radial positional components.The fitting experiments based on the LEO of 300 to 1500 km altitudes show that the fitting UREs of the proposed 16/17/18/18*/19-element ephemerides are better than 10/6/4/5/2.5 cm,respectively.According to the dynamical range of the fitting elements,the interfaces were designed for the 5 sets of ephemerides.The effects of data truncation on fitting UREs are at millimeter level.The total bits are 329/343/376/379/396,respectively.29/15 bits are saved for the 16/17-element ephemerides compared with the GPS16 ephemeris,while 64/61/41 bits can be saved for the 18/18*/19-element ephemerides compared with the GPS18 elements ephemeris.