Table of Content

25 August 2019, Volume 0 Issue 8

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Preliminary analysis of observation quality and positioning precision for BDS-3 satellites

CHENG Junlong, WANG Wang, MA Liye, LIU Wanke

2019, 0(8):  1-7.  doi:10.13474/j.cnki.11-2246.2019.0241

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Analysis of the BDS-3 observation quality is very important for the upcoming relevant applications and research of BDS-3. This paper evaluates the observation quality of BDS-3 satellites, including the carrier noise ratio (C/N₀), pseudo-range multipath, observation noise, and analyzes the precision of single-point positioning (SPP) and short-baseline relative positioning. The results show that the average C/N₀ values for B1I of BDS-3 satellites are 3~4 dB/Hz higher than those of BDS-2 satellites. In terms of BDS-3 satellites signals, the average C/N₀ values for B3I is the highest and the others are sizable. In the aspect of pseudo-range multipath values, B2a/B2b/B3I of BDS-3 working satellite and B3I of BDS-2 satellites are sizable, and those are slightly lower than that of B1I/B1C of BDS-3 satellites and that of B1I/B2I of BDS-2 satellites. As for observation noise, BDS-3 satellites and BDS-2 working satellites are on the same level. The precision of positioning that used BDS-2 and BDS-3 satellites is slightly greater than that only used BDS-2 satellites because of improving the observation geometry.

An inertial assisted monocular front-end model based on weighted pre-integration and fast initialization

ZENG Pan, PAN Shuguo, HUANG Lixiao, WANG Shuai, ZHAO Tao

2019, 0(8):  8-13,19.  doi:10.13474/j.cnki.11-2246.2019.0242

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Aiming at the robustness and high precision of monocular visual inertial positioning system in complex environment and camera dynamic conditions, Improved_VIO, an inertial assisted monocular front-end model based on weighted pre-integration and fast initialization is proposed. Firstly, the visual and inertial measurement data are synchronized, and a high-precision IMU weighted pre-integration model is established to provide inter-frame motion constraints for joint initialization and visual tracking models. Secondly, constructing the visual inertia fusion state vector, and establishing the joint initialization model realize the fast joint initialization of visual inertia coupling. Finally, based on the IMU weighted pre-integration and fast initialization methods, a visual inertia-assisted tracking model is established to effectively improve the robustness of the system. The experimental results show that compared with the traditional visual inertial positioning front-end model, the Improved_VIO improves the accuracy, speed and robustness of monocular visual inertial positioning. The initialization time is shortened to 10 seconds, and the positioning accuracy is improved about 30%.

Fusion of GNSS and accelerometer monitoring data for dynamic monitoring of high-rise buildings

WANG Yarong, HUANG Shengxiang, KUANG Cuilin

2019, 0(8):  14-19.  doi:10.13474/j.cnki.11-2246.2019.0243

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GNSS and accelerometer are the main means for the dynamic monitoring of the environmental load deformation of high-rise buildings. GNSS can offer three-dimensional displacement directly without intervisibility, while it is insensitive to the weak vibration and high-frequency vibration information due to the limitation of accuracy and sampling rate. Accelerometer has the advantages of high precision and high sampling rate, while it can not monitor the low-frequency quasi-static deformation. Aiming at making full use of the complementary advantages of these two sensors, the methods of multi-rate Kalman filter and RTS smoothing are proposed to fuse GNSS and accelerometer monitoring data of high-rise buildings. The results show that this method can weaken the high-frequency noise and improve the sampling rate of GNSS displacement data compared with the single GNSS monitoring technology, it can effectively identify the low and high vibration frequencies of high-rise buildings, and it can also improve the monitoring ability of vibration micro-deformation. Compared with the single accelerometer technology, this method can accurately monitor the low-frequency deformation information of high-rise buildings. This method has the value of good engineering application.

The effect of surface mass loading on coordinate time series characteristics of GPS stations in Greenland region

WANG Hao, YUE Jianping, XIANG Yunfei, LI Lele

2019, 0(8):  20-24.  doi:10.13474/j.cnki.11-2246.2019.0244

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Based on the daily coordinate time series of 12 GPS stations situated in the Greenland with the time span of period from January 2013 to December 2016 under the framework of ITRF2008,and the noise characteristics, velocity field and periodic term amplitude of each GPS coordinate time series before and after surface mass loading correction are analyzed by maximum likelihood estimation.The results show that the optimal noise model of most GPS coordinate time series is white noise+power law noise and white noise + flicker noise.After correcting GPS coordinate time series by surface mass loading deformation, the flicker noise in the up components is obviously increased,and the average velocity is reduced by about 0.36 mm/a. Compared with the vertical components, the surface mass loading correction has relatively minor effects on the horizontal components. At the same time, the amplitudes of the annual and semi-annual signals in the up componentsare reduced by 44.1% and 14.2% respectively.Whereas, the amplitudes of periodic terms in the horizontal components are increased.

RSI-based transient crustal deformation signal detection of GPS coordinate time series

CHENG Guohui, LUO Yong, LIU Bin, KUANG Cuilin

2019, 0(8):  25-29.  doi:10.13474/j.cnki.11-2246.2019.0245

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A transient deformation method based on relative strength index is proposed in this paper. In the case study, RSI is used in the transient deformation detection from the time series of GPS continuous tracking stations located in the Cascadia subduction zone of North America. The results show that the RSI method can effectively detect the transient deformation in each series, and obtain the start time, end time, duration and slippage of the transient deformation. The variance of residual time series with the transient deviation correction are significantly smaller than the ones without the transient deviation correction.

Adaptive particle filter UWB location algorithms considering colored noise

ZHANG Yuan, TAN Xinglong, ZHAO Changsheng, LI Xiaoming

2019, 0(8):  30-33.  doi:10.13474/j.cnki.11-2246.2019.0246

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The traditional Kalman filter algorithm requires that the noise model conforms to the Gauss distribution. In UWB indoor positioning, the observation noise is not only white noise, but also colored noise. Particle filter can deal with the problem of colored noise. The accuracy of particle filter for target tracking is improved by adding adaptive adjustment of likelihood distribution. The advantages and differences of adaptive adjustment of likelihood distribution particle filter and extended Kalman filter in UWB under white noise and colored noise are also studied. The experimental results show that when the observation noise is white, the extended Kalman filter and particle filter can achieve better pedestrian location and tracking; when the observation noise is colored, the adaptive particle filter is better than particle filter and extended Kalman filter.

Content retrieval of large-scale remote sensing images based on dynamic threshold hashing

QIANG Yonggang, XIAO Zhifeng, CHEN Huanhuan, YAN Liyang

2019, 0(8):  34-38,53.  doi:10.13474/j.cnki.11-2246.2019.0247

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With the rapid development of remote sensing earth observation technology in China, the amount of remote sensing image data received and archived has increased exponentially. The traditional retrieval methods are difficult to retrieve the large amount of remote sensing image data quickly, resulting in the lack of breakthrough in remote sensing image retrieval technology, the utilization ratio and utilization efficiency of remote sensing images in China are very limited. In this paper, an innovative hash index method is proposed, which generates the hash codes dynamically according to the spatial distribution of the feature vectors. This method can encode the feature vectors of high-dimensional remote sensing images in low dimensions, greatly reduces the amount of retrieval computation and significantly improves the retrieval accuracy and efficiency of large-scale remote sensing image database. The retrieval experiments on the sky map data set show that the proposed method has a significant improvement in accuracy and retrieval efficiency, and has a great application potential.

Automatic cloud detection of sentinel-2 satellite images based on neural network

YU Changhui, YU Haiwei, ZHANG Wen, MENG Lingkui

2019, 0(8):  39-43.  doi:10.13474/j.cnki.11-2246.2019.0248

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This paper proposed a high accuracy segmentation method of remote sensing image cloud region based on deep learning to overcome the problem of misjudgment of information caused by cloud covering when using sentinel-2 satellite image to extract ground object information. This method constructs a deep neural network model through the preprocessed remote sensing sample data, and automatically extracts high-level image features. Then the image features are input into the classifier to realize the pixelwise classification of remote sensing image, and the cloud coverage matrix is segmented. Finally, the cloud coverage matrix is transformed into a cloud binary map, which is combined with the region of interest to accurately obtain the cloud detection results of the designated region. The method will provide a new idea for automatic cloud detection in irregular region of sentinel-2 satellite images without auxiliary information and human intervention.

A Space-borne GNSS-R DDM waveform classification method for land surface

TU Jinsheng, ZHANG Rui, HONG Xuebao, HAN Mutian

2019, 0(8):  44-47.  doi:10.13474/j.cnki.11-2246.2019.0249

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In the GNSS-R based land surface remote sensing application for the complex scenarios, there are limitations, such as the low signal-to-noise ratio (SNR) and the difficulty for effective information identification. This problem seriously restricts the GNSS-R application in land surface remote sensing. In order to quickly distinguish clutter signals and effective information from massive space-borne GNSS-R land surface data of low SNR, a new method is proposed for the DDM waveform classification based on statistical inductive analysis and the significant level of the space-borne Delay Doppler Map (DDM) correlation peak. Subsequently, this method is utilized to waveform classification the land surface observation data of UK TechDemoSat-1 (TDS-1) satellite. Finally, related comparative analysis for the SNR of the waveform after classification, and the correlation analysis between the classification results and various typical land surface types was accomplished, which demonstrated the feasibility and effectiveness of the proposed waveform classification method.

Point cloud registration algorithm based on feature point method vector

SUN Peiqi, BU Junzhou, TAO Tingye, FANG Xingbo, HE Han, FENG Jiaqi

2019, 0(8):  48-53.  doi:10.13474/j.cnki.11-2246.2019.0250

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In the traditional ICP algorithm, it is necessary to have a good initial position of two points cloud, otherwise it is easy to fall into local optimization when it is on time. Aiming at this problem, this paper proposes a rough registration method based on feature point extraction and pairing to adjust the initial position of the overlapping parts of two points cloud. Firstly, the feature points of the common part of two point clouds are extracted by using SIFT algorithm, and then the feature points of two point clouds are paired according to the Euclidean distance between the feature point method vectors, and the feature point pairs are purified by using the angle of the method vector. Finally, the rotation and translation matrices are solved by means of unit four yuan number method, and the coarse registration is completed. Experiments show that the coarse registration method based on feature point vector can provide a good initial position for the precision registration, and can avoid the phenomenon that the local optimization is caught on time to a certain extent.

Application of semi-supervised discrete potential theory in remote sensing image change detection

XIE Fuding, HE Jiani, ZHENG Hongliang

2019, 0(8):  54-58.  doi:10.13474/j.cnki.11-2246.2019.0251

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With the development of remote sensing technology, change detection for remote sensing image provides an effective method in environmental monitoring, disaster relief and many other fields. However, it is still a challenging problem to develop more effective change detection methods due to the complexity of ground-truth and the difficulty of labeling the samples and so on. This paper proposes a remote sensing image change detection method based on semi-supervised discrete potential theory. The suggested method first uses a new method to label the samples to get the training set, then constructs complex network by KNN approach. Finally, it improves the classical Wu-Huberman algorithm in complex network and divides the network. As a result, the obtained two community structures exactly correspond to the change part and the invariant part. Experimental results show that the change detection method based on semi-supervised discrete potential theory has perfect change detection performance.

Geo-positioning error characteristic analysis of GF-4 imagery under different imaging states

HAN Jie, XIE Yong, LI Huina, MIAO Baoliang, SHI Hongbin

2019, 0(8):  59-62,67.  doi:10.13474/j.cnki.11-2246.2019.0252

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As a geostationary orbit satellite with high-resolution array imaging characteristic in the Chinese high-resolution earth observation system, the geo-positioning accuracy of GF-4 images under different imaging states has always been the concern of researchers and application departments. In this paper, the geo-positioning accuracy of GF-4 images under staring, pitching and rolling imaging states is analyzed by comparing the location information of the same points on GF-4 images with that on Google Earth images. The results show that there are obvious system errors in a single image. The image geo-positioning errors at the same point in staring imaging state are almost the same. The image geo-positioning errors under the pitch and rolling attitudes are proportional to the pitch and rolling angles. The conclusion can provide the data correction and application reference information to the ground data processing department and users.

High-resolution remote sensing image classification by combining deep learning with nDSM

XU Huimin, QI Hua, NAN Ke, CHEN Min

2019, 0(8):  63-67.  doi:10.13474/j.cnki.11-2246.2019.0253

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Although many classification methods for high-resolution remote sensing images have been proposed in recent years, there are still some problems (e.g. misclassification and incompleteness of object boundary) due to high intra-class variance and limitation of spectral information of high-resolution remote sensing images. In this paper, a high resolution remote sensing image classification method is proposed by combining nDSM (normalized digital surface model) data and deep learning framework. Firstly, nDSM data is combined with remote sensing image as an additional band to generate new imagery and produce training samples. Then, the optimized U-Net model is trained on the basis of training samples to obtain the optimal model. Finally, remote sensing images are combined with nDSM data to be the input data, and the trained optimal model is performed to get classification results. Experimental results demonstrate that the proposed method can effectively improve the classification performance in terms of classification accuracy.

Extraction model of winter wheat planting information based on unsupervised classification

WANG Dongli, ZHANG Anbing, ZHAO Anzhou, LI Jing

2019, 0(8):  68-71,77.  doi:10.13474/j.cnki.11-2246.2019.0254

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There are some problems of supervised learning algorithm in remote sensing extraction of regional winter wheat planting information, such as heavy dependence on ground sample data, complex process, too many artificial interference factors and low degree of automation, etc. In order to solve those problems, this paper proposed a model of winter wheat extraction which took unsupervised classification technology as the core and combined with multi-scale technology. It verified the accuracy and validity of the model proposed in this paper that took Xinji city of Hebei province as a typical experimental area and used the GF-1 remote sensing data in 2014. The experimental results show that the overall accuracy of the model is 94.00%, and the Kappa coefficients is 0.88. For winter wheat in the study area, the model can achieve the supervised classification extraction accuracy without training sample data and less artificial interference factors. So, the model can meet the requirements of ground remote sensing monitoring for winter wheat planting information.

Semantic catalogue service sharing of global land cover information

SHI Haibo, SUN Yaqin, GU Hehe, XU Shenglei

2019, 0(8):  72-77.  doi:10.13474/j.cnki.11-2246.2019.0255

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For the lack of semantic description and implicit relational reasoning ability of metadata, users can't obtain the high-matching land cover information and the number of related resources retrieved is small. This paper proposes the land cover semantic catalogue service by adding ontology reasoning mechanism to the OGC catalogue service, and then designs the land cover metadata model based on the national geographic information metadata standard,builds the ontology model of land cover metadata based on it. Then it uses OWL to formalize the ontology model, and implements the semantic catalogue service of surface coverage using Java. Experiments show that the ontology can effectively improve the recall and precision of surface coverage resources.

Manhole cover object detection in remote sensing imagery with deep convolutional neural networks

YANG Mengyuan, LIU Wei, YIN Pengcheng, XIE Meng

2019, 0(8):  78-81,87.  doi:10.13474/j.cnki.11-2246.2019.0256

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Urban component survey is an important task in the development of digital city management. However, the manhole cover information acquisition still has shortcomings such as low efficiency of manual surveying and high leakage rate. To address these problems, this paper proposes an effective method for detecting manhole cover objects in remote sensing images. We redesign the feature extractor by adopting VGG (visual geometry group) and HED (holistically-nested edge detection) side-output module, which can increase the variety of receptive field size. Then, the detection is performed by a multi-level convolution matching network for object detection based on fused feature maps, which combines several feature maps that enables small and densely packed manhole cover objects to produce stronger response. The results show that the proposed method is more accurate than existing methods for detecting manhole cover in remote sensing images.

Application of oblique photogrammetric technique in demolition surveying and mapping

GAO Chunhui, ZENG Yicheng, WANG Zhen

2019, 0(8):  82-87.  doi:10.13474/j.cnki.11-2246.2019.0257

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The conventional method of demolition surveying and mapping measures sides of houses and their appurtenance by outdoor household surveys and then calculates house area within indoor work. However, it is difficult to speed up and increase efficiency without increasing costs to meet the constantly accelerating urban renewal. As an emerging technology in recent years in the international surveying and mapping field, unmanned aerial vehicle (UAV) oblique photogrammetric technique can generate live 3D model automatically, produce large-scale topographic map and collect the sizes and structural elements of ground object based on the model, thus simplifying field work. This article puts forward a new surveying and mapping pattern for old villages demolition based on oblique photogrammetric technique, and tests the validity of this pattern in Beishan village demolition project. The experimental results show that the precision of topographic map, sizes of ground object and area all fit for corresponding criteriaj, and the production efficiency has been improved significantly.

Dam deformation prediction based on EMD and RBF neural network

LIU Simin, XU Jingtian, JU Boxiao

2019, 0(8):  88-91,95.  doi:10.13474/j.cnki.11-2246.2019.0258

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The use of long-term observation data combined with prediction models to estimate the deformation trend of dams is an essential part of dam structure safety monitoring. In this paper, the EMD and RBF neural networks are comprehensively used to study the intrinsic law of nonlinear periodic signal changes in the dam deformation time series. The 4000 data of Xilongchi L022 station is used as the training sample, and the subsequent 80 data is predicted and passed. The statistical analysis of the difference between the predicted result and the measured deformation is used to evaluate the prediction power of the method. The results show that the RMSE in the three directions of N, E, and U are 0.878 6, 0.360 4 and 2.235 mm, respectively. Compared with BP, RBF prediction is better, and it is less affected by data accuracy. MAE and RMSE can be increased by 63% and 57%, respectively, compared with BP. The method of this paper has high computational efficiency and strong generalization ability.

Application of 3D laser scanning technology applying to super-huge and complex steel sculpture engineering construction

ZHU Mingran

2019, 0(8):  92-95.  doi:10.13474/j.cnki.11-2246.2019.0259

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Taking the construction process of Yunding Mansion, the highest building in Shandong province as an example, this paper studies the key technologies of applying 3D laser scanning technology to super-huge and complex steel sculpture. Through 3D laser scanning of super-huge and complex steel sculpture components and comparing with BIM prefabricated model, the assembly error of steel components is detected beforehand, and the error detection technology flow of super-huge and complex steel sculpture components is established. High-precision characteristic error statistic is realized, which provides an effective and advanced monitoring method for super-huge and complex steel sculpture intelligent building, and it has great reference and practical value.

Research on application of mobile 3D laser measurement system in subway operation tunnel disease monitoring

GAO Hong, LI Kai, MA Quanming, HAN Zhisheng, SUN Pichuan

2019, 0(8):  96-101,161.  doi:10.13474/j.cnki.11-2246.2019.0260

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With the continuous development of rail transit construction,the three-dimensional geographic information system is gradually applied in subway-related completion measurement, section measurement and deformation monitoring, which is increasingly favored by urban construction and metro management departments.Due to the complexity of subway management,it takes a lot of time and people to conduct operational monitoring using traditional methods.In order to meet the urgent requirements of the urban planning department and the metro management department for information and 3D visualization of Subway engineering,this paper proposes a mobile three-dimensional laser measurement system integrating multiple sensors.The system integrates sensors such as high-precision 3D scanners and encoders.which can get the profile size of the tunnel quickly and precisely. And can analyse limits, section profiles and tunnel deformations efficiently through complementary software processing.Through the actual project test,the results show that this method can effectively solve the practical problems in subway tunnel disease monitoring, and can be used as a reference for similar subway projects.

Application of handheld 3D laser scanner in quality inspection of industrial components

LI Yi, XU Chao, LIAO Kaixing, ZHAO Hongyao

2019, 0(8):  102-105.  doi:10.13474/j.cnki.11-2246.2019.0261

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Handheld 3D laser scanner has broad application prospects in quality inspection of industrial components due to its portability and ease of use. This paper proposes a quality inspection method for industrial components based on handheld 3D laser scanners from the main steps of pre-preparation, data acquisition and data processing. The actual case is used to verify the accuracy and efficiency of the method, which provides a quick and low-cost method for quality inspection of industrial components.

Design and analysis of cloud platform for landslide monitoring in Heifangtai, Gansu province based on GPS and InSAR data

WANG Yipeng, ZHANG Yongzhi, ZHAO Chaoying, LIU Xiaojie, ZHANG Yingyun

2019, 0(8):  106-110.  doi:10.13474/j.cnki.11-2246.2019.0262

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In this paper, based on the deep research and thinking of the mainstream cloud computing platform technology, a landslide monitoring cloud platform using GPS and InSAR data is designed in view of the large amount and multiple data types of landslide disaster monitoring data. Taking the heifangtai landslide in Gansu province as an example, the risk assessment and analysis of the landslide are also conducted using ArcGIS. Due to the use of Hadoop technology, the platform can significantly improve the efficiency of massive data storage and processing in landslide monitoring, and make beneficial exploration for the further application of cloud computing technology in disaster monitoring.

Research of temporal and spatial characteristics of urban construction land expansion based on multi-source data: a case study of Shunde district, Foshan city

FU Yan, WANG Jianhui, ZHANG Ya

2019, 0(8):  111-115.  doi:10.13474/j.cnki.11-2246.2019.0263

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With the acceleration of China's urbanization, the scale of urban construction land has been further increased, bringing about urban problems such as traffic congestion, environmental pollution, reduction of green space and urban heat island. In this paper, based on the comprehensive use of the 2015 national geographic census data, 2011 and 2013 remote sensing data, through the establishment of urban expansion intensity index, the use of statistical analysis, spatial clustering and other methods, it determines the rapid expansion of construction land in Shunde district in the past four years, the average annual growth rate of 1.8%, including the average annual growth rate of more than 14% in the new urban area. In addition, it is also finds that the expansion mode of the main urban area is mainly filled expansion, among which Daliang street and Leliu street are mainly the expansion of industrial park, Lunjiao street is mainly the expansion of central park, and Ronggui street is mainly the expansion of logistics park.

Research on comparison and selection methods of remote sensing data sources for geographic national conditions monitoring

XIAO Chang, ZHANG Li

2019, 0(8):  116-120.  doi:10.13474/j.cnki.11-2246.2019.0264

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With the rapid development of remote sensing technology, new remote sensing equipment and methods are constantly emerging. The sources of remote sensing data are expanding, the types of remote sensing data are increasing and the quality of remote sensing data is improving. The application of remote sensing data in geographic national conditions monitoring is also more extensive. However, due to the complexity of remote sensing data sources and the huge amount of data, the selection of geographic national conditions monitoring data sources has become a difficult problem. Based on the main objectives of geographic national conditions monitoring, the optimal selection of data sources can be achieved by selecting the main quality factors of remote sensing data sources. This paper uses certain weighting methods to evaluate and compare them. A method of comparison and selection combining subjective expert evaluation with objective entropy calculation is presented. The method generalizes the main quality factors of data sources, constructs the index system, calculates the evaluation value using various weighted factors, evaluates and compares the data sources for geographic national conditions monitoring. Experiments show the effectiveness of this method.

Fire safety supervision GIS for rental housing by combining cloud computing with big data

XU Gang, YANG Libo, PEI Zhenghao, CHEN Jie

2019, 0(8):  121-124,157.  doi:10.13474/j.cnki.11-2246.2019.0265

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China's urbanization process and economic development have achieved remarkable results, but there is no coordination between fire protection construction planning and urban expansion and development. Although existing research has applied GIS technology to fire safety supervision, there is still a problem that information linkage is not strong and the degree of visualization is low. To this end, based on geographic information system technology, this paper effectively integrates geographic information data, multi-source and multi-temporal remote sensing and aerial imagery, patrol data, and rental housing data in urban development zones to establish time-space geographic information big data. We use virtualization, cloud computing and other technologies to establish a dynamic monitoring platform for fire safety in rental housing. This platform provides management department with decision support based on geographic information, which improves the efficiency and scientific management of rental housing. The completion and application of the system is of great significance to the development of the information management of rental housing in China.

Case study of geospatial big data driven by government application

GONG Lifang, LI Aiqin, CHEN Zhangjian

2019, 0(8):  125-129.  doi:10.13474/j.cnki.11-2246.2019.0266

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With the rapid advancement of big data construction, promoting the modernization of governance has become a hot spot of big data application. Combining with the construction of Zhejiang Province's demonstration of geospatial big data application project, this paper focuses on the analysis of the demand for geospatial big data in government application, and combines the bottleneck problems in the current geographic information public service, puts forward the overall framework, main contents and key technologies of the construction of geospatial big data, and further verifies the effectiveness of the project construction through the application of government demonstration.

Comparative analysis of long-term trends on fraction of vegetation coverage in grassland mining area

LI Jing, CUI Lüyuan, YAN Xiaoxiao, YANG Zhen, DONG Jinwei, DENG Xiaojuan

2019, 0(8):  130-134,157.  doi:10.13474/j.cnki.11-2246.2019.0267

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The Hulunbeier area is a famous grassland area in the world. The fragile ecology there has attracted much attention for the influence of human activities and climate factors. This paper takes the Baorixile coal mine area in the heart of Hulunbeier as the study area to analyze the long-term trends on fraction of vegetation coverage(FVC) by maximum value composite method based on NDVI data from 1985 to 2015. The two methods, linear regression and Sen+Mann-Kendall trend, are used to monitor the long-term tendency on FVC spatial and temporal variation. The results show that FVC change trends obtained by the two methods are basically the same. The Sen+Mann-Kendall method is comparatively more sensitive to the improvement and degradation of vegetation coverage than the one-dimensional linear regression method. The study and its results will do help on scientifically evaluating the impact of long-term coal mining and other human activities on land ecology and provide a reference for method choosing on long-term vegetation change monitoring.

Extraction accuracy and stability analysis of different water body index models in GF-2 images

LIU Shuangtong, WANG Mingxiao, YANG Shuwen, YANG Mingze, YANG Lihua

2019, 0(8):  135-139.  doi:10.13474/j.cnki.11-2246.2019.0268

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According to the characteristics of GF-2 satellite image data, two different research areas in Linxia Hui Autonomous Prefecture are selected, and uses single-band threshold method, normalized difference vegetation index method (NDVI) and three other water body extraction index methods (NDWI, SWI, MSWI) water body extraction test in two study areas. the effects of threshold values on the accuracy and stability of water extraction in each water body index model are analyzed and compared. It is found that the single-band threshold method in the study area 1 (urban area) has the highest extraction effect of 71.29%, and the stability is good. The extraction accuracy of the MSWI method in the second (mountain) of the study area is up to 95.76%, and the stability is second to that of the single-band method. This experiment provides a reliable reference for GF-2 images when selecting different models and thresholds for water extraction in different regions.

Forestland change detection based on spectral and texture features

MEI Shuhong, FAN Chengcheng, LIAO Yongsheng, LI Yaran, SHI Yujun, MAI Chao

2019, 0(8):  140-143.  doi:10.13474/j.cnki.11-2246.2019.0269

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The investigation of forest land change can provide accurate spatial information and attribute information for forest law enforcement supervision and forest land "one map" renewal, which is of great significance for forest resources monitoring and management. In view of the time-consuming and laborious situation of large-scale multi-temporal remote sensing images, this paper presents a method of forest land change detection based on spectral and texture features. Taking the northeastern part of Lingshan County as an example, the GF-2 remote sensing images of 20171209 and 20180201 are used to carry out experiments. The results show that, on the basis of reducing manpower input and time cost, this method not only improves the detection efficiency of remote sensing image by more than half, but also achieves more than 77% detection accuracy. This method has certain application value in forest resources census.

Application of aerial survey of low-altitude multi-rotor UAV in beautiful rural planning and construction

CHEN Zhuan, SHI Chenjing, FENG Xiangrui, LI Hongtao

2019, 0(8):  144-148.  doi:10.13474/j.cnki.11-2246.2019.0270

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Planning and measurement play a crucial part in the construction of beautiful village. However, on account of lacking of geographical information on the design site, it has a strong impact on the integrated planning of the beautiful village.With the emergence of drones, adding to its high precision, flexible operation and strong efficiency, it has quickly become a low-cost alternative to traditional surveying and mapping methods. This paper discusses the related technologies of UAV data acquisition, image processing and modeling. The Pix4Dmapper software is used to process the data acquired by the Dhan Phantom 4 Pro UAV, and generate the DOM, DSM and other results in the planning area, and make a 3D model. And on this basis, the relevant planning and mapping software is used to realize the planning model and bird's eye view of the planning area.

Design of remote monitoring application system for water based on machine vision

YAN Cheng, HE Ning, PANG Weiqing, DENG Deying

2019, 0(8):  149-153.  doi:10.13474/j.cnki.11-2246.2019.0271

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In view of the recognition and rapid measurement of the water surface pollution area, a high-precision measurement scheme of the pollution area of the water surface is proposed to meet the pollution demand of the sea area management and the monitoring of the blind spot, by means of electronic measurement and image discrimination. According to the ranging principle of monocular vision based on machine vision, the UAV aerial photography is used to provide the corresponding parameters and the azimuth information of the polluted area for the measurement model. The image segmentation and stacking technique is used to identify the characteristics of the oil pollution area by artificial delineation boundary. Using the visual image processing method which is suitable for the area measurement, a human-computer interaction for measuring the pollution area of the water surface is designed to realize the real-time or later measurement. The experimental results show that the actual flight control measurement and software processing of UAV provide effective basis for water management. the polluted area is simulated, the accuracy of polluted area measurement is 10^-3 square meters, the measurement relative error is less than 5%, and the average relative error of vertical shooting is less than 1.3%.

A new method of building the plane control network of subway shield construction tunnel

WANG Jiawei, XIA Hanyong, YIN Hejun

2019, 0(8):  158-161.  doi:10.13474/j.cnki.11-2246.2019.0273

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At present, the construction control of long and large tunnel shield control is generally used in traditional wire method. There are these problems of error accumulation too fast, the lack of independent inspection points and through the construction of the error caused by the construction conditions and other issues. Based on the requirements of construction and construction of shield construction,this paper explores and implements a new method of measuring the plane control network within the shield tunnel,than details description the new method about control point layout, observation method and data processing analysis. And it analysis the accuracy and reliability with the two methods of the traditional wire measurement. The experimental results show that this method can improve the measurement accuracy of shield tunneling control, improve the reliability of control point and hanging point, and effectively control the through error.

Application of three dimensional laser scanning technology in detecting the construction axis of heteromorphic buildings

XU Yajun, XIE Hairong, YUAN Xiaojun, CHE Honglei

2019, 0(8):  162-164.  doi:10.13474/j.cnki.11-2246.2019.0274

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Aiming at the disadvantages of low efficiency and incomplete expression in the collection of heteromorphic buildings' features by conventional surveying and mapping methods,this paper takes a tall and heteromorphic building in Nantong as an example. The 3D laser scanner is used as the data acquisition equipment to detect the construction axis by setting control points, collecting point clouds outside of the door and processing the data inside of the door. The accuracy is evaluated.As a result, the feasibility of 3D laser scanning technology in detecting the construction axis of heteromorphic buildings is verified.