Table of Content

25 May 2023, Volume 0 Issue 5

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The method of landslide surface displacement monitoring based on UAV aerial survey point cloud comparison

MENG Yongdong, YUAN Changwei, TIAN Bin, CAI Zhenglong, ZHANG Weijie

2023, 0(5):  1-8.  doi:10.13474/j.cnki.11-2246.2023.0127

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The M3C2 (multiscale model-to-model cloud comparison) algorithm can be used to achieve surface monitoring of landslides while the single-point displacement monitoring of landslides cannot reflect the overall displacement status of the landslide. By analyzing the images collected by UAV through SfM (structure from motion), a 3D point cloud model of the landslide is reconstructed.The point cloud comparison algorithm is then used to process the two sets of point cloud data, and the displacement of the landslide area is represented by color and size to identify the surface displacement changes of the landslide. The application of this method in the surface displacement monitoring of actual slopes shows that the M3C2 algorithm can successfully identify the slope change area, capture horizontal/vertical displacement changes of 1 cm, and intuitively reflect the deformation of the landslide surface. This method is suitable for overall monitoring of landslide displacement under complex terrain conditions with an identification accuracy of centimeter-level, and its performance is better than the C2C (direct cloud-to-cloud comparison with closest point technique) algorithm.

Early identification of ridge-top landslide hazards in Jiuzhaigou area using InSAR-LiDAR method

WANG Zhidong, TANG Wei, MA Zhigang, LI Yuchen, YANG Benyong, LI Weiqing, LI Yongxin

2023, 0(5):  9-15.  doi:10.13474/j.cnki.11-2246.2023.0128

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In the area of about 4000 km² in Jiuzhaigou area, synthetic aperture radar interferometry (InSAR) and light detection and ranging (LiDAR) are applied to identification of landslide hazards. A total of 344 landslide hazards are identified, including 114 ridge-top landslide hazards, with a deformation rate of -149~120 mm/a during December 2017 and October 2020.Through comprehensive remote sensing and field investigation, it is concluded that most of ridge-top landslide tend to develop in the NE, SE-faced slope with a gradient of 35°~45°. The most elevation difference of ridge-top landslide range from 100 m to 350 m. The slope material is mostly composed of quaternary (Q) unconsolidated deposit with geomorphology of high mountains and valleys eroded by tectonics. Finally, Taking the Zhongchagou ridge-top landslide as an example, the temporal and spatial analysis of the ridge-top landslide based on InSAR-LiDAR method is carried out, and the deformation, shape and situation characteristics of the ridge-top landslide are obtained. This study verified the accuracy and reliability of the comprehensive use of InSAR and LiDAR technology to identify and analyze ridge-top landslide hazards, which can provide scientific basis and reference for disaster prevention and mitigation of landslide hazards.

Landslide body identification and detection of high-resolution remote sensing image based on DETR

DU Yufeng, HUANG Liang, ZHAO Zilong, LI Guozhu

2023, 0(5):  16-20.  doi:10.13474/j.cnki.11-2246.2023.0129

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Landslide disasters have attracted great attention because of their great destructiveness, and how to quickly and accurately detect landslides has become a major problem. Aiming at the problems of insufficient landslide detection dataset, low accuracy, and incomplete detection of landslide body, this paper combines the advantages of convolutional neural networks (CNN) and Transformer, and adopts the DETR network to realize the automatic detection of landslide body with Transformer as the main body. First of all, in order to solve the problem of insufficient data in the data set, the offline data enhancement method is used to achieve landslide data augmentation; Secondly, the DETR network structure using the encoder-decoder structure performs multi-scale training and prediction of the augmented dataset; Finally, the experimental results are quantitatively evaluated. Experimental results show that the average accuracy(AP)of landslide detection is 0.997,which can accurately identify and detect landslide bodies.In addition,the experimental results also verify that data enhancement can effectively improve the detection accuracy of landslide bodies in the DETR network.

Landslide evolution law considering multiple dynamic environmental factors: a case study of nine landslide areas in Xining city

HU Xiangxiang, KE Fuyang, ZHANG Zhishan, YAO Yongshun, SONG Bao, MING Lulu, YIN Jixin, ZHANG Haihuan

2023, 0(5):  21-26,43.  doi:10.13474/j.cnki.11-2246.2023.0130

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In this paper, using 103-view Sentinel-1A data from 2018 to 2022, the SBAS-InSAR method was used to obtain information on slope-oriented subsidence in Xining city and to study the trend characteristics of slope-oriented subsidence in the landslide area of Xining city and its coupling relationship with factors such as vegetation cover and precipitation in the region. The results show that the sedimentation trend in the landslide area of Xining city shows the characteristics of slope divergence. Specifically, the degree of landslide sedimentation in the Yang slope area changes dramatically, and its sedimentation rate is faster than that of the Yin slope. According to this characteristic of subsidence change, this paper conducted a more profound study and characteristic mechanism:① The lower the vegetation cover, the higher the landslide deformation rate and the sunny slope is more prone to landslide than the shady slope. ② The amount of precipitation may lead to a short-term increase in the deformation variables of the characteristic points, but the overall situation is still decreasing. ③ The threat of features mainly comes from the sunny slope, and the higher slope area tends to be the place where the rate of landslide area is the largest. Therefore, the attention to landslides in Xining city should be more inclined to the southwest slope in the future to improve the alertness of landslide monitoring and warning in this region.

Deformation prediction of treated landslides based on BP neural network optimized by bird swarm algorithm

CAO Xiaoyan, MAN Xinyao, WANG Jiping, MAI Rongzhang, GUO Yunkai

2023, 0(5):  27-31.  doi:10.13474/j.cnki.11-2246.2023.0131

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Landslide deformation is a key evaluation parameter for judging whether the landslide is stable after treatment. Carrying out the deformation prediction of landslide can grasp the stability of the landslide, which is beneficial to the risk analysis of landslide and facilitates the prevention and control of geological disasters. To accurately predict the deformation of the treated landslide, this paper proposes a deformation predicting method of the treated landslide by using the Bird Swarm Algorithm (BSA) to optimize the BP neural network. The deformation prediction model of highway slope in Guangxi Province is established with BSA-BP neural network, and the prediction results of BSA-BP neural network and normal BP neural network are compared and analyzed. It is indicated that the mean square error and correlation coefficient of the prediction results of the BSA-BP neural network are 0.053 4 and 0.997 6, respectively. The mean square error and correlation coefficient to prediction results are 2.225 6 and 0.968 with BP neural network. Bird Swarm Algorithm can effectively improve the prediction accuracy of the BP neural network model. The BSA-BP neural network model can be effectively applied to the deformation prediction of the treated landslide. The research results could provide a reference for risk prediction of the treated landslide in the future.

Sentinel-1 decorrelation assessment based on vegetation index

PAN Jianping, ZHAO Ruiqi, CAI Zhuoyan, YUAN Yuxin, LI Pengxia

2023, 0(5):  32-37,50.  doi:10.13474/j.cnki.11-2246.2023.0132

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Sentinel-1 data has been widely used in synthetic aperture radar interferometry technology and related application fields since it is opened for free in the world, but its short wavelength characteristics make it easy to generate decorrelation in some low coherence areas, especially in vegetation coverage areas. However, the current mainstream post-interference incoherence evaluation strategy may cause data waste and increase research costs, taking into account the polarization mode, this paper establishes the quantitative models of Sentinel-1 coherence and optical remote sensing vegetation index NDVI at VV and VH polarization, and verifies the accuracy of the models in the adjacent validation area, indicating the reliability of the model. Based on the established models, the decorrelation can be quantitatively evaluated before the interference of Sentinel-1 data, in order to overcome the shortcomings of the above post-interference evaluation strategy, improve the efficiency of interferometry and reduce the research costs.

Construction of green wave correction normalized water body index for GF-7 images

MA Haichao, WANG Chongchang, HE Zhaoning, XIN Sheng, WANG Lei

2023, 0(5):  38-43.  doi:10.13474/j.cnki.11-2246.2023.0133

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Based on the construction of high-resolution GF-7 satellite image water index, taking Pukou district of Nanjing as the research area, a green normalized difference vegetation index (GNDVI) is proposed, and the accuracy is compared with the single-band threshold method, NDVI, NDWI, CWI and SBI. The results show that the overall accuracy of the GNDVI index method is higher than that of other methods, reaching 97.26%, the water body information is more complete, and the suppression of mountain and building shadows is better, and the effect is optimal. It provides a usable technical reference for GF-7 remote sensing images in the application and research of water resources protection.

Remote sensing classification of wetlands in regions around the South China Sea based on bilinear graph convolutional network

LI Xinyuan, HE Zhi, LOU Anjun, XIAO Man

2023, 0(5):  44-50.  doi:10.13474/j.cnki.11-2246.2023.0134

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Wetlands have important carbon sink functions, and play a key role in purifying water quality and regulating climate. There exist abundant wetland resources in regions around the South China Sea, and it is of great significance to monitor wetlands in this area to promote the cross-border joint protection of coastal wetlands in China and achieve the goal of carbon dioxide peaking and carbon neutrality. This paper proposes a wetland classification method based on BiGCN using object-oriented hierarchical classification. Random forest is used to distinguish wetlands from non-wetlands firstly, and then wetlands are sub-classified by the BiGCN. In the BiGCN, the methods of constructing bilinear model, optimizing graph structure and using better activation function are used to further optimize network performance. The results show that the overall classification accuracy of the proposed model is above 92% on the three Sentinel-2 data sets around the South China Sea, which is more than 4% higher than that of the existing graph convolution network, and the time consumption is greatly reduced.

ICESat-2 and multivariate optical image for tidal flat topography inversion methods

WANG Zihe, KANG Yanyan, WANG Minjing

2023, 0(5):  51-55,61.  doi:10.13474/j.cnki.11-2246.2023.0135

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In response to the problems of large operational difficulties, high price and low accuracy of tidal flat topography monitoring, based on ICESat-2 satellite laser point cloud profile data, this paper proposes a digital elevation model inversion method combining multivariate optical images. The two major sandbar systems of the Tiaozini and Gaoni in the Yellow Sea of China are used as research objects. Using low-pass filtering techniques to extract topographic height values and combine them with tidal water level data to assign values, the conversion from 2D to 3D is achieved, and improves the inversion accuracy of the current mainstream waterline tide level assignment method. The model shows more detail of the topographic variation of the tidal flat surface, with significant differences in height, allowing the presence of clear tidal trench topography to be observed. Comparing the measured data, the correlation coefficient is 0.89 with a root mean square error of 0.34 m. The results of this study demonstrate the significance of ICESat-2 data for the inversion of beach topography and lay the data foundation for the construction of the theory of tidal flats evolution under the influence of human activities.

Semantic segmentation of indoor 3D point cloud by joint optimization of geometric features and neural networks

YAO Mengmeng, LI Xiaoming, WANG Weixi, XIE Linfu, HUANG Junjie, HUANG Hongsheng, TANG Shengjun

2023, 0(5):  56-61.  doi:10.13474/j.cnki.11-2246.2023.0136

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A precise semantic segmentation of indoor 3D point cloud is the basis for realizing deep applications of interior space. To address the problem of incomplete and inconsistent segmentation objectives of existing semantic segmentation methods for 3D point clouds. In this paper, an semantic segmentation method for point clouds is proposed, it uses geometric features of point clouds and deep neural networks. First of all, it uses deep learning to achieve the initial extraction of semantic labels of indoor structural information. Secondly, it uses the segmentation method of point cloud with geometric features and color features to accurately segment the original data.Finally, a probabilistic model has proposed to cross-validate the initial segmentation results with the segmentation results of geometric features to achieve joint optimization of the results for semantic segmentation. The accuracy and validity of the segmentation method proposed in this paper are verified based on open-source datasets, and three sets of indoor point cloud data from simple to complex indoor scenes are tested respectively, and the experimental results show that the method proposed in this paper can effectively improve the semantic segmentation accuracy of the indoor 3D point cloud.

Image registration method for improving the positioning accuracy of HISEA-1 SAR image

ZHU Linhong, ZHONG Ruofei, WANG Ya, LI Qingyang

2023, 0(5):  62-66.  doi:10.13474/j.cnki.11-2246.2023.0137

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HISEA-1 is the first domestic C-band commercial SAR satellite bench marking with international advanced indicators. The highest imaging resolution is 1 m, and the whole satellite mass is less than 185 kg. It has the advantages of light, small, low cost and flexible scheduling. However, the positioning accuracy of the image products is still far behind that of domestic and foreign satellite images with high geometric accuracy, such as Sentinel-1 interferometric wide band mode image products. This paper presents a method to improve the geometric accuracy of SAR image by using image registration technology. Sentinel-1 data with known high-precision geographical coordinates is used as the reference datum, and HISEA-1 SAR data is used as the image to be registered. By completing the accurate matching between the two images, the positioning accuracy of HISEA-1 SAR image is corrected.

Automatic road marking extraction method based on vehicle LiDAR point cloud and panoramic image

NIU Pengtao, CAO Yi, ZHANG Enchao, QI Yang

2023, 0(5):  67-71,139.  doi:10.13474/j.cnki.11-2246.2023.0138

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In view of the traditional artificial way, the man-machine interactive way to extract road marking cost is high, the problem of low efficiency, put forward integration vehicle LiDAR point clouds and panoramic images as the data source, using SCGA-Net network to extract the road marking method, vector quantization and to solve the vehicle in the process of the LiDAR points clouds gathering caused by vehicle cover, such as road repair data missing problem. Experimental results show that the extraction rate and accuracy of road markers are better than the method of only using LiDAR point cloud to extract road markers, and can effectively improve the production efficiency of high-precision maps that automatic driving relies on.

Mapping and positioning of 3D LiDAR SLAM algorithm in mine environment

ZHANG Qingyu, CUI Lizhen, DU Xiuduo, MA Baoliang

2023, 0(5):  72-77.  doi:10.13474/j.cnki.11-2246.2023.0139

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Aiming at the problems of mapping distortion and large cumulative error in mine terrain mapping, a 3D LiDAR-based synchronous positioning and map construction algorithm is studied. Firstly, the internal and external parameters of the LiDAR and IMU are calibrated to solve the problem that maps cannot be built. Then, the inspection robot collects data sets of three sets of scenes (block roads, mine slopes, and mining areas), compares the mainstream algorithms such as ALOAM, LeGO-LOAM, and LIO-SAM, and uses GNSS data as the true trajectory value. The test shows that the multi-sensor fusion algorithm LIO-SAM has better robustness and positioning accuracy in the mine environment, and the absolute position error of the trajectory is reduced by 21.53% and 60.10% compared with ALOAM and LeGO-LOAM, respectively. In addition, the feature extraction part introduces strength features so as to error of the IALIO algorithm is 20.16% lower than that of LIO-SAM.

Hyperspectral inversion of soil organic matter in tensile fracture zone of coal mining

ZHANG Quanwang, GUO Hui

2023, 0(5):  78-83,134.  doi:10.13474/j.cnki.11-2246.2023.0140

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The tensile cracks caused by coal mining destroy the soil structure and affect the soil quality. Using hyperspectral technology to monitor the important components of the soil in the fracture zone is of great significance for accurately restoring the soil quality in the fracture zone and improving agricultural production. In this study, 90 groups of soil samples are collected in the tensile fracture area of coal mining in Zhuzhuang coal mine in Huaibei, and the spectra of soil samples are measured indoors. The reflectance values are correlated with the measured organic matter content, and the characteristic bands sensitive to organic matter are selected. Partial least squares and BP neural network are used for modeling, and the accuracy of each model is evaluated. The results show that the inversion effect of this study is ideal, and the first-order differential and partial least squares model (FD-PLSR) has the best modeling effect. The R² of FD-PLSR model are 0.876 1、0.845 9, and the RMSE of FD-PLSR model are 0.497 2、0.680 6, respectively. The research can provide some technical support for the monitoring of soil organic matter content in tension fracture zone of coal mining.

Tracking methods of space station based on SGP4 model

WANG Xiao, LI Weichao, YANG Xuhai, QIN Weijin, WANG Wei, LI Zhigang

2023, 0(5):  84-89.  doi:10.13474/j.cnki.11-2246.2023.0141

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The successful launch of the Core Module of China Space Station marks that the on-orbit assembly and construction of the China Space Station has entered the stage of full implementation.When the China Space Station entered the predetermined orbit,it is the premise for obtaining scientific data that realizing the tracking observation of the ground station to the China Space Station.The calculational algorithm that the tracking angle of the China Space Station observed by ground station is studied based on existing SGP4 model according to the two line orbital elements of the China Space Station in this paper.And compute the tracking angle of the Xi'an station to observe the China Space Station,the difference is less than 0.001 degrees compared with the results by STK software,Which proves the correctness of the calculational algorithm.Furthermore,the effective prediction time of two line orbital elements is studied on aforementioned basis.All results above provide the support for the ground station to observe low-orbit targets such as the China Space Station.

Improved OSELM localization algorithm in dynamic environment of underground coal mine

DOU Zhanshu, CUI Lizhen, HONG Jinxiang, SHI Mingquan

2023, 0(5):  90-95.  doi:10.13474/j.cnki.11-2246.2023.0142

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It is important to obtain the location of underground operators in a timely and accurate manner in the complex and changing underground coal mine environment, where the highly dynamic changes in the underground communication environment lead to a decrease in model localization accuracy. In this paper, the online sequential extreme learning machine (OSELM) algorithm is used for underground localization. Compared with the batch-type localization algorithms GA-BP and extreme learning machine (ELM), the OSELM algorithm can maintain the original model localization accuracy more effectively. However, the OSELM algorithm still suffers from the deficiencies of sickness matrix inversion and equal treatment of all added data, which leads to the poor stability and adaptability of the algorithm to dynamic environments. Based on the OSELM algorithm, the regularization OSELM algorithm and the forgetting factor OSELM algorithm are proposed respectively, while the OSELM algorithm that integrates the regularization technique and the forgetting factor mechanism is proposed. The experiments show that the localization accuracy of the OSELM algorithm is 0.566 and 0.628 2 m higher than that of the GA-BP and ELM algorithms, respectively, after the change of the experimental environment; the localization accuracy of the proposed OSELM algorithm with regularization and forgetting factor is higher than that of the OSELM algorithm in the 3 m error distance range; the localization accuracy of the OSELM algorithm that fuses the two mechanisms is the highest. The localization accuracy of the OSELM algorithm with the fusion of the two mechanisms is about 5% higher than that of the OSELM algorithm. Both the proposed OSELM and its improved algorithm can effectively improve the model localization accuracy.

A regional tropospheric zenith wet delay model considering piece wise representation for China region

HUANG Ning, FU Shiyang, ZHANG Lulu, XIONG Ling, HUANG Liangke, LIU Lilong

2023, 0(5):  96-100.  doi:10.13474/j.cnki.11-2246.2023.0143

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High-precision tropospheric zenith wet delay (ZWD) plays an important role in GNSS high-precision positioning and atmospheric water vapor monitoring. China has the characteristics of vast territory and variable terrain, and there are regular and difficult to follow airflow changes in the vertical direction. However, most ZWD models only use a single function to fit the changes in the atmospheric height range, or do not consider seasonal factors, so the applicability in China is poor. In this paper, based on the atmospheric reanalysis data of China's regional MERRA-2, an in-depth study of ZWD is carried out, and a Chinese regional ZWD model (CZWD model) is established to takes into account the subsection expression. The accuracy of the model is tested by taking the ZWD data calculated by the integration of 89 sounding stations in China as the reference value. The results show that the annual average Bias and annual RMS errors of the CZWD model are -2.9 and 21.9 mm, respectively. The accuracy is improved by 5% than GPT3 model, showing better accuracy and applicability in the Chinese region as a whole. Therefore, the CZWD model is of great significance for regional GNSS navigation and positioning and water vapor monitoring in China.

Fast and accurate tracking algorithm for natural feature points of bridge

HE Yuwei, ZHUGE Sheng, XU Xiangpeng, ZHONG Lijun, YANG Xia, ZHANG Xiaohu

2023, 0(5):  101-106,163.  doi:10.13474/j.cnki.11-2246.2023.0144

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Feature point tracking is the critical technique of visual measurement of bridge deflection by UAV, the speed and precision of the tracking algorithm will directly affect the efficiency and accuracy of the measurement. In this paper, the initial location results of the feature points in the image sequence are obtained through surf feature matching, and then the feature points are accurately registered using the phase correlation method. An acceleration strategy based on the motion continuity of UAV is proposed to track the natural feature points of the bridge. The performance of the presented approach is verified through an image sequence of the bridge captured by a UAV, and the results of the experiment show that the average speed of the proposed algorithm is 25 FPS, and the tracking accuracy is sub-pixel level, which meets the measurement requirements, and provides technical support for visual measurement.

Ecological sensitivity study of oasis supported by GIS: a case study of Yili river valley

CHEN Wanji, ZHAO Yang, CUI Dong, Lü Shaolun, LU Jirui, Muyassar Saydahma

2023, 0(5):  107-114.  doi:10.13474/j.cnki.11-2246.2023.0145

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In order to explore the protection methods of oasis ecological environment and solve the contradiction between oasis ecological environment and social and economic development, this paper selects seven ecological sensitivity evaluation factors, combines with analytic hierarchy process and mean square error decision method to determine the weight of each factor, and evaluates and analyzes the ecological sensitivity of Yili river valley. Then, four social and economic indicators are selected to analyze the correlation between ecological sensitivity and social economy in Yili river valley through spatial autocorrelation analysis using Moran index. The results show that:① Altitude factors and vegetation factors have the strongest impact on ecological sensitivity in the Yili river valley. ② The ecological sensitivity of Yili river valley is moderate in general, mainly affected by vertical zonality, and has a positive correlation with altitude and slope. Affected by natural environment, sensitivity is negatively correlated with water and vegetation. ③ There is a negative spatial correlation between ecological sensitivity and social economy in Yili river valley. The research on the correlation between ecological sensitivity and social economy provides certain theoretical guidance and reference for the future ecological protection, territorial space planning and social and economic development of Yili river valley.

Application of 3D laser scanning technology to wing micro deformation detection

ZHU Zan, YAO Xing, WANG Jianqi, LI Yang, LONG Shike

2023, 0(5):  115-119.  doi:10.13474/j.cnki.11-2246.2023.0146

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During the flight of an aircraft, the wing and fuselage usually produce local and structural small deformation that can not be directly recognized by human eyes, which will damage the flight aerodynamic performance of the aircraft. To solve this problem, in the research, the 3D laser scanning technology which can achieve millimeter reverse modeling is applied to the micro deformation detection of aircraft wing. Through the designed scheme, the technical links of the whole process, such as point cloud acquisition, point cloud registration, filter denoising, packaging modeling and deformation index extraction, are explored, and the feasibility of the whole technical process is verified. At the same time, the accuracy of 3D laser scanning technology applied to wing micro deformation detection is evaluated through comparative analysis experiments. The experimental results show that the comprehensive detection accuracy of this technology can reach 0.12 mm, which can reach the nominal accuracy of the experimental scanning instrument.

Extraction and analysis of self-built house in Dongguan based on 3D real scene

ZHANG Shiran, CHEN Minghui, HUANG Yan

2023, 0(5):  120-124,163.  doi:10.13474/j.cnki.11-2246.2023.0147

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UAV tilt photography technology can quickly obtain abundant and comprehensive building texture, and plays an important role in the extraction of self-built housing. This research is based on the UAV tilt photography technology producing 3D real scene, which intuitively shows and extracts the self-built housings in Dongguan, as a results establishes a self-built housing database, so as to realize the "coordination and management of all self-built housing information like moves on a chessboard". By applying GIS, the scales, spatial distributions, building structures and population living habits of self-built housing are quantitatively obtained. The analysis displays that Dongguan has a large number of self-built housing, which occupies a lot of land resources, has potential safety hazards and carries a huge number of residents. The houses are clustered along old towns, urban trunk roads and rivers. This research can provide a reference for the government to formulate relevant policies.

Building change detection using tilted image reconstruction point cloud

HUANG Hua, GE Weiliao, LIU Weiwei, QIAN Rongrong, LI Jie

2023, 0(5):  125-129.  doi:10.13474/j.cnki.11-2246.2023.0148

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Detecting building changes is an important task to analyze the change of urban spatial layout. To address the problem of noise or complicated boundary in the process of change detection using satellite images,this paper researches on the automatic extraction of change information of buildings for plane and height from the point cloud reconstructed by photogrammetry techniques. Firstly, the cloth simulation filtering algorithm is proposed to reduce the influence of terrain, and then a deep learning technology of dynamic graph neural network is used to effectively extract the building points. The change regions of buildings are extracted through the comparison of two point cloud classification results. This paper selects the dense matching point clouds of two phases of tilt photogrammetry in some areas of Xiaoshan District, Hangzhou for experimental analysis and verification.The results demonstrate that the proposed method can quickly realize reliable building change detection in a wide range, the change information of plane and elevation dimensions of buildings is well reflected,and can provide support for fine urban management.

Application of SSA-BP neural network in UAV point cloud hole repair

Lü Fuqiang, TANG Shihua, ZHANG Yan, SONG Xiaohui, HU Pengcheng, LI Zhu

2023, 0(5):  130-134.  doi:10.13474/j.cnki.11-2246.2023.0149

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In order to solve the problem of hole repair in UAV point cloud data, a back-propagation neural networkhole repair method was proposed based on sparrow search algorithm (SSA). The sparrow search algorithm was used to optimize the initial weight and threshold of the traditional BP neural network, and then the BP neural network algorithm (SSA-BP) optimized by the sparrow search algorithm was applied to repair the holes in uav point cloud data. In order to verify the feasibility of the algorithm, the accuracy of SSA-BP neural network was compared with that of traditional BP neural network and least square support vector machine (LSSVM) algorithms. The experimental results show that the repair accuracy of SSA-BP neural network algorithm is higher than the other two groups of comparison algorithms, and the SSA-BP neural network is more stable, and it still has a good repair effect in the repair of complex terrain holes.

Calculation method of step height in open-pit mining area based on UAV image matching point cloud

Lü Xinyang, FAN Dongli, ZHANG Guangyun, TAN Ding

2023, 0(5):  135-139.  doi:10.13474/j.cnki.11-2246.2023.0150

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The calculation of step height in open-pit mine is an important part of mine survey and acceptance, which is of great significance to strengthen mine production monitoring and cost management.In view of the problems of the traditional step height measurement method, such as heavy workload, poor efficiency and difficulty in ensuring the safety of surveyors, this paper proposes a step height calculation method considering the stratified structure of slope based on the open pit point cloud obtained by the tilt photography of UAV. In this method, the flat area is eliminated by surface element division, the slope point cloud is processed by density clustering, and the top line and bottom line of the step are obtained by random sampling consistency. Finally, the height of the step is calculated. The experimental results show that it can measure the step height efficiently and accurately in open-pit mine, which has important application value for mining operation and safety.

Calculation method and its application of open-pit mineral reserves of a hybrid interpolation algorithm

WANG Lu, LU Xiaoping, LI Guoli, LI Xiaolei, KANG Aifeng

2023, 0(5):  140-147.  doi:10.13474/j.cnki.11-2246.2023.0151

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A hybrid interpolation algorithm is proposed for 3D modeling of the geological body to meet the demand of open pit mine reserve estimation. Firstly, the Erdaozhuang open pit mine in Hebi is selected as the study area for practical application, and the obtained borehole data are stratified and processed. Based on the spatial autocorrelation analysis of the processing results, a hybrid algorithm of kriging interpolation, regular spline interpolation, tension spline interpolation and inverse distance weighted interpolation is used for encryption, and the optimal interpolation method for different geological layers is determined after cross-validation. Then, we use Revit and Dynamo for visual programming and adopt the modeling idea of "point-surface-body" to model the mine geological body in 3D. Finally, a mineral reserve estimation system is established based on the secondary development of C/S in SuperMap platform. The results show that the method can meet the requirements of mine reserve calculation and provide reliable data support for mine planning, production control, scientific management and intelligent mine construction.

Ecological protection and restoration of resource-exhausted cities territorial space from ecological degradation risk perspective: taking Dayu county as an example

ZHANG Xiaoping, HU Zihong, ZHANG Lu

2023, 0(5):  148-152,169.  doi:10.13474/j.cnki.11-2246.2023.0152

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Under the background of transformation and development pressure and limited ecological resilience, resource-exhausted cities will still face certain risks of ecological degradation, and ecological protection and restoration will face severe challenges. The study takes Dayu county, Jiangxi province as the research area. It identifies the overall ecological degradation risk and its spatial characteristics, divides the types of ecological protection and restoration areas, and proposes ecological protection and restoration strategies. The research results show that:① Dayu county has significant spatial agglomeration characteristics of ecological degradation risks, showing a spatial pattern of "four areas and one area". ②The overall level of ecological degradation risk in the county is not high, and the local ecological degradation risk is mainly caused by ecological pressure, among which mining is the key factor leading to the increase of local ecological pressure. ③Dayu county is divided into ecological management areas, ecological conservation areas, ecological conservation areas and ecological improvement areas. The overall ecological protection and restoration should be carried out based on the characteristics of different regions to promote the systematic promotion of ecological protection and restoration of land space in resource-exhausted cities.

Path planning of UAV 3D environment based on improved ant colony algorithm

DONG Zhiyang, LI Hui, GE Jingyu, CHENG Jianhua

2023, 0(5):  153-157.  doi:10.13474/j.cnki.11-2246.2023.0153

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Aiming at the problems of slow planning speed and easy to fall into local optimization when the traditional ant colony algorithm is used for path planning of UAV 3D environment, this paper proposes three improved strategies:changing the state transition rules with the guidance function, the prior distribution of the initial pheromone, and the time-varying pheromone update method, fully mining the prior information of path planning, enhancing the path by adding the guidance function, and increasing the probability of selecting the optimal path. At the same time, the pheromone is given a different initial concentration according to the distance from the prior path, so that the algorithm has a clear direction in the initial search. The pheromone is updated based on the idea of survival of the fittest, and the pheromone volatilization factor is set as a fluctuation factor that obeys the Laplace distribution, so as to avoid the search process from falling into local optimization, maximize the path search efficiency, and realize the path planning of UAV in the 3D environment. The simulation results show that the improved ant colony algorithm is superior to the traditional ant colony algorithm in planning the optimal path length and searching efficiency.

Application of the rail transit planning and route selection in digital twin city

WANG Fenqi, LIU Ting, ZONG Erkai

2023, 0(5):  158-163.  doi:10.13474/j.cnki.11-2246.2023.0154

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Digital twin city has played an important role in ensuring the smooth progress of urban modernization.This paper takes the third round construction planning of Ningbo rail transit as an example,and demonstrates that the static and multi temporal digital twin city can meet the application needs of urban planning and construction.It systematically expounds the construction method of the unified big scene of the digital twin city.Digital twin city integrates multi-source and multi-scale data, such as 2D data, landform, real 3D model, rail transit information model, underground pipeline model, etc.Use digital twin city for two-dimensional and three-dimensional line selection of rail transit.Connect the design, construction, operation and maintenance after the connection planning and line selection.Rail transit life cycle management forms a perfect closed loop in digital twin city.In the future, digital twin city will accelerate their expansion to other fields and has broad prospects.

CryoSat-2 radar altimeter sea ice waveform preferred feature classification

WU Bin, WANG Zhiyong, LI Xing, TIAN Kang

2023, 0(5):  164-169.  doi:10.13474/j.cnki.11-2246.2023.0155

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Monitoring the type change or thickness change of sea ice is a more effective way to monitor sea ice. In this paper, the CryoSat-2 radar altimeter is used to study the types of Arctic sea ice. Using radar altimeter to classify Arctic sea ice, on the one hand, it is difficult to select the optimal characteristic parameters, on the other hand, it is difficult to achieve a relatively refined classification of sea ice with a single radar altimeter data. In view of the above problems, this paper constructed a feature selection method using the combination of chi-square test, mutual information and Wrapper packing method. The feature subset (SSD+Sigma0+LTPP+PP+SK+LEW) selected by this method is combined Random forest classification divides the radar altimeter data in the Arctic into seawater, one-year thin ice, one-year thick ice, and multi-year ice. The correct classification rate of this method is 93.32% in the training set, 92.42% in the validation set, and the Kappa coefficient is 0.90, all of which are better than other feature combinations, which can basically achieve effective classification of sea ice in the Arctic region, and the classification results can also help in the inversion of sea ice thickness.

Discussion on error theory and data processing course teaching under interdisciplinary background

WANG Chisheng, LI Qingquan, TU Wei, YUE Yang, XIA Jizhe, LU Min, LIANG Shihao, GAO Qing, GUO Renzhong

2023, 0(5):  170-174,179.  doi:10.13474/j.cnki.11-2246.2023.0156

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Error theory has always been the basic core content of surveying and mapping and also the support of information geography such as geographic information science and remote sensing. In the past, the teaching of error theory mainly focuses on the specific measurement formulas and problems of edge measurement, angle measurement and height measurement. However, the development of science and technology, surveying and mapping science and electronics, computers, urban planning and other fields present a trend of disciplinary crossing and integration, the students of this major are less directly engaged in the traditional surveying and mapping industry. As the basic theory of data processing and error theory still plays a very important supporting role in students' knowledge learning in the fields of spatial statistics, navigation and positioning, multi-sensor fusion, spatio-temporal big data application and so on. This paper analyzes the significance of the course of error theory and data processing, discusses the challenges faced by the classical curriculum system under the background of interdisciplinary development, and puts forward a new preliminary teaching reform plan. Finally, we take the teaching practice for the undergraduate students major in geospatial information engineering in Shenzhen University as an example to carry out the analysis.

Extraction of yardang landforms boundary based on multi-spatial resolution Google Earth image and Canny edge algorithm

HAN Yang, YUAN Weitao, LAI Zhongping, CHENG Shixiu, LIU Wenke

2023, 0(5):  175-179.  doi:10.13474/j.cnki.11-2246.2023.0157

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The morphological characteristics of yardangs can reflect its development process and evolution stage, which are very important for the study of yardang landform. However, there are few high-precision and low-cost methods for boundary extraction of yardangs at present. In this paper, The Canny edge algorithm is adopted to resample high spatial resolution Google Earth image (1.19 m) to a series of different lower spatial resolutions images (3、5、8、10、12、15 m). Then yardang boundaries are extracted using Canny edge algorithm with different spatial resolution images. Finally the different results are combined together, and good results have been achieved. The results show that:①Although shadows cannot be identified, the overall accuracy of Canny edge extraction method is 89.23%, and the Kappa coefficient is 0.72, which is similar to the accuracy obtained by medium segmentation scale (138) with object-oriented method. ②Using the Canny edge extraction algorithm, the extracted median width of yardang has a good linear relationship with the spatial resolution of the image (R²=0.95). With the decrease of the spatial resolution, the total length of yardang boundary extracted by the Canny edge algorithm shows a significant logarithmic decrease (R²=0.904).

Application of UAV-borne LiDAR in surveying and mapping 1: 500 topographic map of photovoltaic field area

TIAN Zhengjie, WANG Xueying, QU Tao, LI Zehui

2023, 0(5):  180-184.  doi:10.13474/j.cnki.11-2246.2023.0158

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The design of photovoltaic power station requires the higher precision of the plane and elevation in the PV field area. To avoid the terrain and land classification that cannot be occupied the design of photovoltaic power station needs to map 1:500 scale topographic map. Nowadays, the low-altitude UAV aerial survey is the common used technical means for PV field area mapping. However, this method is difficult to collect the high-precision ground elevation information in the sheltered area, and easy to result in large errors and cause losses to design and construction. In response to the above situation, a reasonable and efficient mapping scheme is developed in this paper, the low-altitude UAV-borne LiDAR system is used to conduct all-round data acquisition for the PV field area, then generates high-precision DEM and DOM products which are used to make 1:500 scale topographic map finally. Through the field accuracy comparison and analysis, the 1:500 scale topographic map can meet the design requirements of the PV field area perfectly, and the problem of large errors of the general aerial survey, which cannot express the micro-topography effectively, is solved in the vegetation shielding area.