Table of Content

25 July 2022, Volume 0 Issue 7

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Remote sensing monitoring of coastal aquaculture ponds in Beili island from 1995 to 2019

DONG Di, WEI Zheng, ZENG Jisheng

2022, 0(7):  1-6.  doi:10.13474/j.cnki.11-2246.2022.0194

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It is of great significance to monitor coastal aquaculture ponds, especially for marine resource management, ecological environment protection, disaster prevention and mitigation. This paper uses Landsat 5, SPOT 5 and GF-1 satellite imagery as data source, and selects Beili island in Guangdong province as the research area. Linear spectral unmixing method is applied to obtain the area of coastal aquaculture ponds based on medium spatial resolution satellite imagery, whereas object-oriented multi-scale segmentation and support vector machine classification algorithm is used to extract coastal aquaculture ponds based on high spatial resolution satellite imagery. The results demonstrate that compared with a single satellite data source, the multi-source medium and high spatial resolution satellite imagery extends the traceable time span of the coastal aquaculture water surface change analysis and improves the monitoring accuracy; the combined spectral unmixing and object-oriented classification methods can be used to monitor coastal aquaculture ponds for long time series. In the past two decades, the area of coastal aquaculture ponds in Beili island first increased and then slowly decreased. The average growth rate of the coastal aquaculture pond area is 23.39 hm² from 1995 to 2000, 23.95 hm²/a from 2000 to 2006, and -1.96 hm²/a from 2019 to 2006.

Prediction of the outbreak scale of Enteromorpha prolifera in the Yellow Sea based on historical data

LIU Lu, LUO Nianxue, ZHAO Qiansheng

2022, 0(7):  7-11.  doi:10.13474/j.cnki.11-2246.2022.0195

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According to the historical data of initial coverage area and the maximum coverage area of Enteromorpha prolifera in yellow Sea, we selected the precipitation, temperature, light intensity, the three most important influence factors for the growth and diffusion of Enteromorpha prolifera, proposed and established a modle which base on BP neural network to determine the conversion coefficient R to prediction Enteromorpha prolifera coverage. It can realize the simulation and prediction of the maximum coverage area of Enteromorpha prolifera in this year at the early stage. By using historical data to validate, the results showed that the prediction of Enteromorpha prolifera maximum outbreak scale was consistent with the real situation. The results of this study can provide some references for the emergency preparation of Enteromorpha prolifera for more time.

Remote sensing investigation on water pollution of pond aquaculture in estuary of Maowei Sea

HU Yiqiang, YANG Ji, JING Wenlong, PENG Xiaoyan, LAN Wenlu, PENG Mengwei, ZHANG Yumeng

2022, 0(7):  12-17,53.  doi:10.13474/j.cnki.11-2246.2022.0196

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Aiming at the pollution of pond breeding in mouth of Mawei Sea in Guangxi, this paper based on unmanned aerial vehicle (UAV) multi-spectral remote sensing images and measured water quality data, establish spectral characteristics and remote sensing inversion model of five water quality parameters, which can reflecting the nutritional state of water bodies: Chlorophyll-A (Chl-A), Chemical Oxygen Demand (COD), Suspended Solid (SS), Total Nitrogen (TN) and Total Phosphorus (TP). Based on the inversion results, we evaluate the eutrophication status of water body. The results show that: ①Chl-A is significantly correlated with Blue and NIR bands, COD is highly correlated with Red and Red Edge bands, SS is highly correlated with Red Edge bands, TN issignificantly correlated with NIR bands, TP is highly correlated with Blue and Green bands. ②Among the established inversion models of water quality parameters, Quadratic polynomial function inversion model has the best fitting effect.③We found that the eutrophication index of water bodies in pond aquaculture area mainly ranged from 60 to 80, belonging to moderate and severe eutrophication degree, and the eutrophication degree of near-shore water bodies was lower than that of far-shore water bodies in spatial distribution.

3D road boundary extraction based on mobile laser scanning point clouds and OSM data

WANG Yanjun, LIN Yunhao, WANG Shuhan, LI Shaochun, WANG Mengjie

2022, 0(7):  18-25.  doi:10.13474/j.cnki.11-2246.2022.0197

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Accurate road boundary extraction modeling is an important topic in urban road management, intelligent traffic planning and high-precision map generation. In this paper, an accurate 3D road boundary extraction method based on OSM is proposed based on mobile laser scanning point clouds data. Firstly, the original mobile LiDAR point cloud data is processed by CSF filtering to separate the ground points, and the candidate data set of road boundary points is obtained by combining with the relative elevation analysis. Then, the nodes of OSM vector road network data are used to assist the data segmentation of road boundary point candidate point set. Finally, a 3D road boundary point set is obtained based on RANSAC algorithm in each segment point cloud data set. Through the extraction experiment of three different types of urban road boundary sections, the analysis results show that, the accuracy rate and recall rate of the proposed method are 96.12% and 95.17%, respectively, and F1 value is 92.11%. The research method in this paper can be used to extract and vectorize high-precision road boundaries, thus provide support for intelligent transportation and unmanned navigation.

Detection model construction based on CNN for offshore drilling platform and training algorithm analysis

LIU Lin, SUN Yi, LI Wanwu

2022, 0(7):  26-32,99.  doi:10.13474/j.cnki.11-2246.2022.0198

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Convolutional neural networks (CNN) is the most representative network structure of deep learning (DL). Synthetic aperture radar (SAR) image itself has the position structure relationship, the characteristics of the CNN model determine that it can use the position structure relationship of the image to extract the features of the image better,so it is more suitable to use the CNN model for marine target detection.The paper based on CNN framework constructs the DL model Ocean TDAx of offshore drilling platform detection, trains and tests the OceanTDAx model through the improved WinR-Adagrad gradient training algorithm, experimental results show that the Oceant TDA9 model is the highest accuracy. For the Ocean TDA9 model, seven model training algorithms, such as adam, RMSprop, Stochastic gradient descent (SGD), Adagrad and Momentum, is used to conduct experiments,and the training loss and accuracy with relevance of training batches of different algorithmsis is compared. Based on the polarized SAR data of the Bohai sea, the proposed Ocean TDA9 model and the existing CNN model and Visual geometry group (VGG) model are used to compare the detection experiments of offshore drilling platforms.The results show that the constructed Ocean TDA9 model is excellent overall performance in drilling platform testing.

Application of CatBoost model in water depth inversion

KONG Ruiyao, XIE Tao, MA Ming, KONG Ruilin

2022, 0(7):  33-37.  doi:10.13474/j.cnki.11-2246.2022.0199

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In multispectral remote sensing water depth inversion research, the traditional water depth inversion models have some limitations due to many factors affecting the accuracy of water depth inversion. Machine learning algorithms are more advantageous in solving nonlinear and highly complex problems, and their application in some specific areas of water depth inversion can improve the inversion accuracy. In this paper, using Sentinel-2 multispectral remote sensing images and LiDAR bathymetry data, it constructs CatBoost water depth inversion model with Oahu as the study area and compares the inversion accuracy with traditional water depth inversion models as well as XGBoost and LightGBM models in Boosting. It's showed in the experimental results that R-Square, root mean square error, mean absolute error, and mean relative error of the tuned CatBoost water depth inversion model are 96.19%, 1.09 m, 0.77 m and 9.61%, and the accuracy of the model is the highest, and the effect is more better.

Study on inversion of forest biomass by LiDAR and hyperspectral

WEN Yuxiao, Lü Jie, MA Qingxun, ZHANG Peng, XU Ruling

2022, 0(7):  38-42.  doi:10.13474/j.cnki.11-2246.2022.0200

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Estimating forest aboveground biomass (AGB) is critical to achieving global carbon neutral goals. In this study, Howland forest in Maine, USA is taken as the research area. With the ground measured sample site data, different data sources (airborne LiDAR and hyperspectral remote sensing data) and machine learning algorithms (random forest, support vector machine, gradient boosting decision tree and K-nearest neighbor) are compared and analyzed. It is to improve the estimation accuracy of Howland forest AGB. The results show that the optimal accuracy of the model with airborne LiDAR and hyperspectral vegetation index variables is 0.874 and 0.868 respectively. The accuracy of the regression model with the combination of airborne LiDAR and hyperspectral vegetation index variables and gradient boosting decision tree is 0.927, that is, multi-source remote sensing data is better than a single data source. The synergistic use of LiDAR and hyperspectral data has applicability and application prospects for improving the accuracy of biomass estimates in areas such as Howland and beyond.

Rapid extraction of COVID-19 information based on nighttime light remote sensing: a case study of Beijing

JIANG Zelin, DENG Jian, LUAN Haijun, LI Lanhui

2022, 0(7):  43-48.  doi:10.13474/j.cnki.11-2246.2022.0201

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In response to the increasingly complex epidemic situation at home and abroad, this paper takes the Beijing epidemic in June 2020 as an example, use the day-by-day luminous data of NPP-VIIRS satellite to analyze the affected areas of the epidemic and the status of epidemic control and recovery, and to explore the relationship with social and economic factors such as population density. The research results show that day-by-day luminous remote sensing can effectively extract the area affected by the epidemic and reflect the degree of impact of the area. The extracted affected area is consistent with the medium and high risk areas designated by the country; At the same time, It can timely monitor the development of the epidemic and the status of epidemic prevention and control measures, the intensity of regional luminous has changed significantly with the progress of the epidemic and prevention and control; The average light loss and recovery intensity have a strong correlation with the regional population density during the outbreak period, and the correlation index R² is 0.97 and 0.91 respectively. The results of this study show that day-by-day luminous imaging has great potential in quickly extracting information on changes in the new crown pneumonia epidemic.

Adaptive pose estimation for robot based on extended Kalman filter and point-line iterative closest point

YUE Shengjie, WANG Hongqi, LIU Qunpo, ZHAO Rongliang

2022, 0(7):  49-53.  doi:10.13474/j.cnki.11-2246.2022.0202

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In this paper, a method of robot pose correction based on multi-source information fusion using extended Kalman filter (EKF)and point-line iterative closest point(PL-ICP)point cloud matching algorithm is proposed to solve the problems of large cumulative error and low accuracy of single pose estimation method for robots. In order to reduce the cumulative error caused by the wheel odometer, Mahony algorithm is used to calculate the attitude of the gyroscope and accelerometer. The preliminary estimation of robot pose is obtained by fusing the wheel odometer based on the extended Kalman filter. In order to reduce the influence of wheel deformation and slip on the pose of the robot, using the PL-ICP point cloud matching algorithm to construct a single-line laser odometer to estimate the robot pose again; In order to improve the accuracy of pose estimation, based on the total mean square error of the two poses and the pose error at the time before and after, an adaptive correction algorithm for the cumulative error is constructed. The method in this paper obtains global optimal weight factor and local dynamic weight factor to realize the adaptive adjustment of the cumulative error correction factor by analyzing the total mean square error of the two poses and the pose error at the time before and after, which can obtain more accurate pose estimation of the robot. Experimental results show that this method can correct the cumulative error of the robot's pose, and significantly improve the accuracy of robot pose estimation.

Super-resolution reconstruction method based on self-similarity and edge-preserving decomposition

ZHENG Yan, HE Huan, BU Lijing, JIN Xin

2022, 0(7):  54-59.  doi:10.13474/j.cnki.11-2246.2022.0203

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In view of the poor effect of edge detail information reconstruction in remote sensing image super-resolution reconstruction, a super-resolution reconstruction method based on self similarity feature and edge feature preserving decomposition is proposed. Firstly, in order to make full use of the similarity information of the original low resolution image, the local self similarity reconstruction method is used to obtain the initial reconstruction results of the image. In order to further increase the edge information of different scales, the weighted least square method is used to decompose the initial reconstruction results, and the decomposed detail layers are weighted linear combined. Finally, the super-resolution reconstructed image integrating multi-scale edge, detail information and local similarity features is obtained through optimization calculation. The results show that this method can effectively improve the edge information and detail information of remote sensing images.

Application of ground SAR in landslide deformation monitoring

ZHOU Zhiwei, CHENG Xiang, ZHOU Wei, HAO Weifeng, XIAO Haibin, CHEN Hongjie, YANG Kui

2022, 0(7):  60-63.  doi:10.13474/j.cnki.11-2246.2022.0204

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Ground based InSAR is a new technical means developed in recent years to mesure surface deformation based on ground-based SAR (GB-SAR). It has high resolution, and can monitor in real time, and achieve millimeter deformation monitoring accuracy. It provides an advanced technical means for real-time monitoring and early warning of short-range landslide. This paper takes a landslide in Lancang River as the study object, a fixed station is set up on the opposite bank of the landslide to collect GB-SAR data according to the fixed frequency. The interferogram pair is formed by two images successively, then the high coherent point target is extracted by the coherent threshold method, and finally the deformation result of landslide is extracted by the deformation model. The research shows that the GB-SAR is able to obtain the whole deformation boundary, spatial distribution and time change process of the landslide, which is very effective for real-time monitoring of landslide disaster. The relevant research of this paper can provide references for landslide disaster monitoring and early warning.

Monitoring land subsidence of Hebei section of Beijing-Xiong'an intercity railway by time-series InSAR

GE Pengfei, LIU Hui, CHEN Mi, LI Yu, DING Ruili, LIU Fei

2022, 0(7):  64-70.  doi:10.13474/j.cnki.11-2246.2022.0205

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In this paper, the spatial and temporal distribution of land subsidence along the Beijing-Xiong'an intercity railway (Hebei section), from Gu'an station to Xiong'an station, is obtained by using 34 Sentinel-1B images from 2018 to 2020 based on SBAS-InSAR technology. Then, the spatial autocorrelation analysis is used to understand the spatial distribution characteristics of land subsidence in the study area. Finally, the causes of subsidence are analyzed combined with the hydrological data. The results show that, the development of land subsidence along Hebei section of Beijing-Xiong'an intercity railway is different from north to south. The average annual settlement rate in the north is less than 10 mm/a, and the maximum annual settlement rate in the south is -105.6 mm/a, and the average annual settlement rate in the west is higher than that in the east. By analyzing the influencing factors, it can be concluded that there is a correlation between ground subsidence and groundwater depth, and the area with high groundwater depth has a higher ground subsidence. At the same time, combined with the results of land use change in the study area, it is found that the static load caused by urbanization has a certain impact on the land subsidence along the Beijing-Xiong'an intercity railway.

Multi-track InSAR 3D deformation monitoring for mining areas by optimizing priori model parameters

SHI Jiancun, YANG Zefa, WU Lixin

2022, 0(7):  71-77.  doi:10.13474/j.cnki.11-2246.2022.0206

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It is crucial to measure the 3D components of ground surface deformation caused by underground coal mining, in order to control mining-related geohazards and understand mining deformation mechanism. In view of the problems of low precision and poor time resolution in the fusion of single-track InSAR and prior models to solve the three-dimensional deformation of mines, this paper proposes an multi-track InSAR 3D deformation solution method for the mine areas based on optimized priori model parameters. The method firstly uses the Weibull model to synchronize the line-of-sight(LOS) deformation displacements of the four tracks SAR datasets,thereby reducing the effect of time asynchrony between different tracks on the accuracy of the 3D deformation solution,and the weighted least squares is used to solve the vertical and east-west deformation of the mine area.Then,a priori model parameter is optimized according to the vertical and east-west displacements.Finally,the north-south displacements are solved based on the prior model.The proposed method is validated using TerraSAR-X data and three tracks Sentinel-1 datasets covering the mining area of Datong. The results show that the proposed method can obtain relatively accurate 3D deformation results (vertical,east-west and north-south RMSE are approximately 1.1,1.4 and 2.1 cm,respectively) when compared to the GPS measurements.

GPS multipath effect snow depth estimation by Gaussian process regression-assisted

JIA Xiuli

2022, 0(7):  78-82.  doi:10.13474/j.cnki.11-2246.2022.0207

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This article studies the global positioning system- interferometric reflectometry technology. Based on the GPS monitoring data of P101 station provided by the Plate Boundary Observatory in the United States, it utilizes the obvious feature of multipath effect when the altitude angle of GPS satellite is lower than certain angle, constructs Gaussian process regression-assisted(GPR-assisted)GPS interference reflection snow depth estimation model and monitors the snow depth around GPS stations. The results show that the accuracy of the snow depth estimation value output by the GPR-assisted GPS interference reflection snow depth estimation model is improved to varying degrees compared with the traditional single-satellite inversion result, and the change trend of GPR snow depth estimation value is closer to the change of actual snow depth, which provides a new idea for surface snow depth inversion.

The modeling of RSSI ranging temperature correction in temperature variation environment

XI Guangyong, GAO Jun, ZOU Dongyao, SHAO Xiaoya

2022, 0(7):  83-86.  doi:10.13474/j.cnki.11-2246.2022.0208

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Most of the existing path loss models of RSSI ranging rely on empirical models and have poor adaptability to the environment.Temperature variation is one of the main factors affecting RSSI observation and the accuracy of RSSI ranging.Therefore,the establishment of signal propagation path loss model including temperature correction term is the key to improve RSSI ranging accuracy in temperature variation environment.Based on the log-distance path loss model,two modeling methods of temperature correction which regard path loss index as a function of temperature and RSSI direct temperature correction are analyzed,and three specific temperature correction models of RSSI ranging are proposed.Using the measured data of RSSI from temperature variation experiment,the characteristics of RSSI with temperature variation are analyzed,and the RSSI ranging temperature correction models is established.The results show that the RSSI ranging temperature correction models established respectively by polynomial correction term and mixed correction term have high ranging correction accuracy,but with the distance between nodes increases,the modeling error increases.

A natural scene construction method for digital twin cities

HE Biao, GUO Renzhong, ZHANG Chen, MA Ding, WANG Weixi, HONG Wuyang, CHEN Yebin

2022, 0(7):  87-92.  doi:10.13474/j.cnki.11-2246.2022.0209

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The digital twin city seeks to restore the urban scene that is close to the reality and provides immersive experience based on the completeness of the urban entity. Natural scene plays an important role in urban scenes. However, natural scene construction faces many challenges due to its diverse types, complex structure and indeterminate location of the scene components. With the development of digital twin city, natural scene construction requires not only high-quality 3D models of scene components but also their ecological characteristics such as growth processes, structural compositions and distribution rules in the natural environment to cope with scene simulations at different scales. Moreover, these structural-functional models result in an in-depth scene visualization that facilitate both better spatial cognition and immersive experience. This paper identifies the key issues of natural scene construction from the perspective of the scene construction process, reviews the relevant, state-of-art techniques and methods of 3D scene construction, and further proposes a natural scene construction framework for digital twin city.

A self-global-best-fit road network Stroke generation method

Lü Zheng, SUN Qun, WEN Bowei, MA Jingzhen

2022, 0(7):  93-99.  doi:10.13474/j.cnki.11-2246.2022.0210

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Stroke plays an important role in road network research. At present, most stroke connection strategies focus on the local and don't consider global performance. The paper presents a self-global-best-fit strategy for generating road network Stroke. Firstly, the Stroke tree is built under the constraints of connection rules with the selected road as the root node. Then, the best Stroke of each tree is selected by the random forest algorithm. The results show that the performance of the self-global-best-fit strategy is better than the self-fit strategy, self-best-fit strategy, and every-best-fit strategy in visual cognition and network function.

Spatial differentiation characteristics and driving factors of landscape ecological risk in arid area of northern Shaanxi

ZHONG Qikang, WANG Zhiyi, WANG Na, XI Furui

2022, 0(7):  100-106.  doi:10.13474/j.cnki.11-2246.2022.0211

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The Loess Plateau in northern Shaanxi is an ecologically fragile area and a key national ecological restoration project area. Taking the land use data of Yulin city in northern Shaanxi from 2000 to 2020 as the data source, this paper uses the grid method to construct a comprehensive ecological risk index model based on landscape pattern, analyzes the spatial and temporal distribution of landscape ecological risk quantitatively in Yulin city, and uses geographic detectors, R and Origin software and other tools to analyze the coupling relationship between land use and ecological risk. The results show that: from 2000 to 2020, the ecological risk in this area tends to decrease as a whole, the area of low ecological risk area increases, the area of high ecological risk area gradually decreases and shifts to the northwest; the area of medium risk area increases year by year, and the area still exists strong potential ecological risks. Changes in woodland and grassland areas under the influence of human activities and natural stress are the main factors driving the changes in landscape ecological risks.

Spatial distribution and analysis of debris flow in Jishishan county of Gansu province

QIANG Dexia, MA Haizheng, ZHU Ziping, GOU Yanmei

2022, 0(7):  107-111,117.  doi:10.13474/j.cnki.11-2246.2022.0212

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Debris flow disasters are sudden, rapid and serious, easy to cause serious losses to people??s lives, property and social infrastructure construction. Taking the rainfall data when mudslide occurred in Jishishan county from August 2 to 3, 2018 as the critical value, combined with the data of environmental variables such as terrain and geomorphology, soil quality, vegetation coverage and hydrology in the region, it is found that the areas such as Liuji township, Shiyuan township and Dahejia township of Jishishan county are prone to high risk areas of debris flow disasters through data analysis with GIS technology. There are three main causes of local mudslides. ①The local vegetation is seriously damaged so that the soil is unstable and heavy rainfall easily causes debris flow. ②The research area belongs to the Loess Plateau landform, and debris flow is easy to occur under the erosion of water. ③ The damage along the Yellow River is more serious. In the flood season of summer and autumn, the water volume of the Yellow River increases sharply, and the soil without vegetation protection is severely eroded, providing material conditions for the occurrence of debris flow.

Tree canopy delineation using UAV multispectral imagery

RAN Chongxian, LI Senlei

2022, 0(7):  112-117.  doi:10.13474/j.cnki.11-2246.2022.0213

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As one of the main components of trees,the canopy is an important parameter for tree growth and tree species identification, which is of great significance to forest resource survey and ecological research. Compared with traditional field surveys, UAV remote sensing technology is more efficient and convenient. This paper is based on UAV multi-spectral image for canopy extraction. We use local maximum algorithm and Mean Shift optimization for tree detection, whose detection accuracy is about 10% higher than the local maximum method. In addition, we design a new tree canopy delineation algorithm,which use dynamic programming algorithm to extract the global optimal boundary. Compared with the watershed segmentation algorithm, the proposed method has better results in sparse or denser forest. The F-score is increased by 12% in the sparse, and the F-score is increased by 28% in the desne.

A new method of remote sensing interpretation production based on integration of human-machine and intelligence

LIU Li, DONG Xianmin, LIU Juan, WEN Xuehu

2022, 0(7):  118-123,137.  doi:10.13474/j.cnki.11-2246.2022.0214

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Based on the basic concept of deep learning and the deep integration of surveying and mapping production technology, this paper explains the changes in the existing remote sensing interpretation production process brought about by the cross-border integration of surveying and mapping geographic information technology, and analyzes the challenges faced by intelligent production in the existing software and hardware environment. A new method of remote sensing interpretation production based on human-computer fusion intelligence is proposed, which breaks through multiple key technologies such as multi-GPU parallel training, rolling feedback training, and distributed micro-service applications, and developed a natural resource deep learning remote sensing intelligent solution. Translation platform and natural resources deep learning dynamic interpretation plug-in, and large-scale production applications have been carried out in key production links of surveying and mapping engineering projects such as the construction, maintenance and update of global geographic information resources. Through multiple projects, it has been verified that machine intelligence and human intelligence can be efficiently integrated through progressive human-computer interaction in remote sensing interpretation production, which greatly reduces the workload of production personnel and improves the scientificity and timeliness of remote sensing interpretation. It provides strong technical support for surveying and mapping production and natural resource survey and monitoring work.

Analysis of land use and coverage change and driving force in Hulan district of Harbin city in recent 20 Years

AI Min, JING Hui, TIAN Yudong, GUO Lanqin, PEI Yuanjie

2022, 0(7):  124-128.  doi:10.13474/j.cnki.11-2246.2022.0215

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Based on Landsat TM image data and socio-economic data from 1998 to 2018 in Hulan district of Harbin city, the characteristics of land use change in Hulan district of Harbin city during 1998—2010, 2010—2018 and 1998—2018 are analyzed scientifically and systematically by means of ENVI, ArcMap and other software, using methods of supervisory classification, land use change model and canonical correlation analysis. The results show that: in the past 20 years,the change of the whole land cover area in Hulan district of Harbin city reflects the change pattern of land use with the increase of residential land and water area, a downward trend in cultivated land area, and the decrease of grassland and woodland. Moreover, the change speed and range in the first 8 years are greater than that in the last 12 years. The main reasons for the change of land use types in Hulan district can be divided into natural factors, socio-economic factors and population factors.

A method for accurate extraction of gated electric towers based on airborne laser point cloud

LIU Yuxian, RUAN Minghao, YAN Zhen

2022, 0(7):  129-133.  doi:10.13474/j.cnki.11-2246.2022.0216

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With the development of unmanned earial vehicle(UAV) technology, more and more power departments use airborne light detection and ranging(LiDAR) technology and oblique photogrammetry technology to replace the traditional manual method gradually, and carry out power inspection, power component maintenance and other work. As a common high-voltage tower in the power corridor at present, accurate point cloud extraction is an important step in the daily maintenance of the gated electric tower in the unlabeled grid point cloud data. In this paper, starting with the grid point cloud data including the ground, vegetation, power line and portal tower, the point cloud is divided into voxel and voxel grid. In order to remove the ground point and vegetation point, the line elevation is continuous and the elevation threshold is filtered, and the top of the tower is extended to search. Then the horizontal projection straight line of the gated electric tower is obtained by using the characteristics of the horizontal projection of the gated electric tower, and the redundant power line point cloud is removed by using the distance threshold judgment. The method introduced in this paper realizes the accurate extraction of the gated electric tower from the unlabeled grid point cloud data, and provides data basis for further three-dimensional modeling, maintenance and repair of the gated electric tower.

Detection and application of urban road disease based on ground penetrating radar+3D measuring endoscope

LIU Guochao, PENG Weiping, YANG Shuihua, HU Zhouwen

2022, 0(7):  134-137.  doi:10.13474/j.cnki.11-2246.2022.0217

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As an important hidden danger of urban safety,urban road diseases has the characteristics of concealment and sudden,which needs to be detected and treated in time. Ground penetrating radar uses electromagnetic wave reflection to determine the distribution of underground media. It is widely used in urban road disease detection because of its nondestructive,fast and shallow high-resolution advantages. However,if the disease body needs to be further treated,it needs to be further carefully surveyed. This paper combines the advantages of ground penetrating radar in road disease detection with the real-time video and measurement function of three-dimensional measuring endoscope technology to realize the accurate positioning and accurate measurement of road underground disease body,so as to provide useful data for subsequent prevention and treatment.

Feature extraction of spatial and temporal change of land in functional areas based on weighted full-polarization SAR image classification

YANG Jun, LIU Linghui

2022, 0(7):  138-142,153.  doi:10.13474/j.cnki.11-2246.2022.0218

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In order to accurately analyze the spatial-temporal evolution of land in functional areas, it is necessary to accurately distinguish the relevant characteristics of land spatial-temporal change. This paper designs the feature extraction model of land spatial-temporal change in functional areas. By using full polarization decomposition and gray level co-occurrence matrix,all kinds of scattering features and texture features of different objects in SAR image which reflect the spatiotemporal change of land in functional area are classified, and the best weighted full polarization feature combination is determined.The combination is input into random forest model to complete the classification of ground objects in the final image and realize the feature extraction of spatiotemporal change of land in functional area. The test results show that: the model uses weighted full polarization feature combination,which can accurately describe the distribution of surface features and ensure the reliable classification of surface features.Taking the spatial-temporal change characteristics of land in an ecological function area of Hunan province as an example, it can achieve good extraction effect.

Application of GNSS+INS integrated navigation in mine auto-driving trucks

ZHANG Bo, LIU Qiang, XU Guoliang, CAI Renlan, CHENG Feng

2022, 0(7):  143-147.  doi:10.13474/j.cnki.11-2246.2022.0219

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For the problem of insufficient anti-jamming capability of GNSS and INS cumulative error in autonomous driving scene of mine truck, this paper proposes a combined navigation algorithm based on GNSS and INS. This algorithm combines the advantages of the two algorithms to improve positioning precision and reliability. This article compares the RTK algorithm and the integrated navigation algorithm with the NovAtel onboard output and the open source software RTKLIB results. The results show that the accuracy of the algorithm in this paper is close to that of NovAtel's onboard output, and is better than the open source software RTKLIB. The average plane and elevation error and STD in this paper are better than 5 cm, and the average attitude error and STD are better than 1°, which can meet the positioning accuracy requirements of autonomous mining trucks.

Discussion on quality inspection technology for new fundamental surveying and mapping

HAN Wenli, ZHANG Jixian, CHEN Haipeng, HUANG Haiying, ZHANG Libo, GE Juan, SHEN Jing, LU Yao

2022, 0(7):  148-153.  doi:10.13474/j.cnki.11-2246.2022.0220

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In view of the technical requirements and challenges brought to quality inspection by the innovation of concept, technology, product and organization mode in new fundamental surveying and mapping, aiming at improving the automatic and intelligent quality inspection ability, this paper constructs the quality inspection technical framework of new fundamental surveying and mapping. And proposes the research content and direction of modern quality inspection infrastructure and equipment, new theory and common technology, real-time data processing and quality inspection technology,networked service mode.Promote the application of big data, knowledge map, blockchain, cloud service and other technologies in new fundamental surveying and mapping quality inspection. Improve the quality assurance ability and level of new fundamental surveying and mapping, and support the transformation and high-quality development of fundamental surveying and mapping.

Security protection technology and verification analysis of geographic information data based on TEA algorithm

TAO XiaoJing

2022, 0(7):  154-157,167.  doi:10.13474/j.cnki.11-2246.2022.0221

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Geographic information data is an important basic data for national defense construction and national economic development. With the development of technologies such as digitization, informatization and cloud computing, the security threat of geographic information data is becoming more and more serious. Aiming at the challenges of secure storage, efficient transmission and real-time information decryption and processing of geographic information data on mobile platform. A geographic information data security protection technology based on TEA algorithm is proposed. This technology uses cyclic iterative shift and XOR operation to encrypt and decrypt geographic information data, which can realize high-security encrypted storage and high-performance real-time decryption processing of geographic information data on mobile platform. This algorithm is used to process and analyze SRTM data. The experimental results show that the geographic information data encryption technology based on TEA algorithm can effectively realize the encryption and decryption of geographic information data, and meet the requirements of mobile system for high-security storage and high-performance processing of terrain data.

An intelligent identification method of user identity based on asynchronous trajectory

CAI Roudan

2022, 0(7):  158-162,167.  doi:10.13474/j.cnki.11-2246.2022.0222

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Traditional trajectory identification methods have limit in feature selection and accuracy. Therefore, this paper proposes a mixed neural network of convolutional neural networks and recurrent neural networks(ConvGRU-Bidir).Firstly, the one-dimensional CNN and one-dimensional pooling neural networks will compress trajectory data and extract high-dimensional features. Then, the bidirectional GRU learns trajectory features from time positive and reverse simultaneously. Finally, the model can recognize users' ID number. This paper uses the GeoLife trajectory dataset to train and test the model, which contains 10837 trajectory samples from 122 users. The results show that the model has an identification accuracy of 97.28% for asynchronous trajectory data, which has improved by at least 30% compared with the existing methods, which proves deep learning's availability and effectiveness in such problems.

Cultivation of innovation ability of surveying and mapping students based on multi-platform integration

HUANG Liangke, WANG Shitai, ZHOU Lü, FU Bolin, LU Qin, LIU Lilong, LI Jingwen, JIANG Jianwu, HUANG Junsheng

2022, 0(7):  163-167.  doi:10.13474/j.cnki.11-2246.2022.0223

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Three platforms of “science and technology innovation”“social practice” and “science and technology competition” are regarded as the starting point and goal, aiming at cultivating the innovation ability of surveying and mapping students in this paper. Analyzing of the realization path to improve the innovation ability of surveying and mapping students by multi-platform development, as well as expanding collaborative education mechanism and the effect of integration on the cultivation of innovation ability, a suitable pattern for the cultivation of innovation ability of surveying and mapping students is explored to provide experience for improving the innovation ability of surveying and mapping students in other local universities over China.

Precision analysis of single tree parameter extraction for multi-platform point cloud data

ZHOU Ye, LIU Yunbo, ZHENG Libo, LONG Yangjun

2022, 0(7):  168-172.  doi:10.13474/j.cnki.11-2246.2022.0224

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Four representative street trees (magnolia grandiflora, sapindus mukorossi, platanus acerifolia, cinnamomum camphora) in Haining city, Zhejiang province are selected as research objects. Combined with unmanned aerial vehicle(UAV) images and 3D laser scanning data, the point cloud splicing, filtering, noise reduction and editing are completed by using ContextCapture and LiDAR 360 software, and the point cloud fine matching is achieved by iterative closest point algorithm. Complete multi-platform point cloud data fusion, obtain digital surface model and digital elevation model and make canopy height model on this basis; Watershed segmentation algorithm is used to segment CHM of different street tree species, and local maximum method is integrated to extract parameters of single tree height and crown width. The data show that the proposed method can achieve high precision of single tree segmentation, and the extraction effect of tree height and crown width is good, which meets the requirements of street tree geometric parameter investigation.