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Table of Content

    20 February 2021, Volume 50 Issue 2
    Geodesy and Navigation
    Tightly-coupled integration of acoustic signal and MEMS sensors on smartphones for indoor positioning
    CHEN Ruizhi, GUO Guangyi, YE Feng, QIAN Long, XU Shihao, LI Zheng
    2021, 50(2):  143-152.  doi:10.11947/j.AGCS.2021.20200551
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    Based on the built-in sensors of a smartphone, a tightly-coupled integrated indoor positioning solution was developed based on the acoustic ranging signal and measurements from other built-in sensors. First, a two-step time of arrival (TOA) estimation method based on short-time Fourier transform and enhanced cross-correlation is performed to achieve an accurate TOA estimate. Having obtained the accurate TOA estimates, studies on a tightly-coupled integrated navigation algorithm based on TDOA and PDR is carried out. The algorithm takes the advantage of the complementarity of PDR and acoustic ranging observables to effectively improve positioning accuracy in a dynamic positioning scenario. In order to evaluate the performance of the proposed algorithm, field tests for static case and dynamic case were carried out. In the static test case, an average positioning accuracy of 0.238 m was achieved with an improvement of 38.66% compared to the least square solution purely based on acoustic ranging observables. For the dynamic test case, a method based on predicted state of Doppler compensation was applied for Doppler corrections of the TDOA observables. Furthermore, asynchronous TOA compensation, which caused by the fact that TOA measurements are not estimated in the same epoch, was also applied before feeding to the positioning algorithm. The test results demonstrated that positioning accuracy in dynamic case is 0.513 m. Compared to the solution without applying the Doppler corrections and the asynchronous TOA compensations, the performance is improved by 27.64%. Three mobile phones (Huawei Mate 20, OnePlus 6 and Google Pixel 3) were used in the field test, the performance of the positioning algorithm is consistence in all three different phones.
    Curvilinear coordinates from the gravitational field and the related application in orientation and navigation
    YU Jinhai, XU Huan, WAN Xiaoyun
    2021, 50(2):  153-159.  doi:10.11947/j.AGCS.2021.20200216
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    Three independent functions are introduced from the gravimetry and gradiometry, so a curvilinear system of coordinates from the gravitational field can be constructed. Then potential applications of the coordinates in orientation or navigation are discussed. To realize orientation or navigation for some object, the observation equations are established by using the gravimetry and gradiometry, and the coordinates of the object can be solved from the equations. Since the observation equations are nonlinear, the linearization for them and the iterative arithmetic are required. To examine the accuracies of the orientation or navigation by using the curvilinear coordinates proposed in the paper, some arithmetic examples are given with the help of EGM2008 gravity field model, and the computational results illustrate that the proposed method is feasible.
    Detect and repair cycle-slip by reconstruction Doppler integral algorithm and STPIR
    CAI Chenglin, SHEN Wenbo, ZENG Wuling, YU Honggang, XIE Xiaoping
    2021, 50(2):  160-168.  doi:10.11947/j.AGCS.2021.20190327
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    It is necessary to ensure that there is no cycle-slip in the carrier phase for precise positioning. Currently, one cycle-slip is still a difficult problem in double-frequency positioning technology. MW (Melbourn-Wübbena combination) method combined with ionospheric residual method is widely used to detect cycle slip, but the accuracy of MW method is insufficient. Therefore, we develop a novel method called reconstruction doppler integral to replace the MW method. Based on the Doppler model of the signal transmission, it can acquire precise Doppler values with the results only by using the broadcast ephemeris and pseudo range. In order to further improve the performance of the proposed method, reconstruction of dual frequency combined Doppler is proposed and integrated. But this method has the same blind zone of cycle-slip detection as the MW method, so we integrate the STPIR (second-order time-difference phase ionospheric residual) algorithm into the detection method above. We validate the proposed method for the cycle-slip detection on data of two types sampling rate (e.g., kinetic 1 Hz and static 1/30 Hz) containing the simulated cycle-slips. The results show that the detection error of the reconstruction Doppler integral method is smaller than that of the MW method. In conclusion, our method can accurately detect and repair any integer cycle-slips under the above conditions, and the higher the sampling rate, the higher the precision.
    GPS/BDS/Galileo precise orbit determination using triple-frequency uncombined observation model
    ZENG Tian, SUI Lifen, RUAN Rengui, JIA Xiaolin, XIAO Guorui
    2021, 50(2):  169-180.  doi:10.11947/j.AGCS.2021.20200159
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    The navigation satellite of the four global navigation satellite system (GNSS) transmitting multi-frequency signals becomes a prevailing trend. In this contribution, a triple-frequency (TF) uncombined (UC) precise orbit determination (POD) method based on IGS clock datum is developed and its ambiguity resolution strategy is proposed. The hardware delay of carrier phase is divided by time-invariant and variant components. Then the UC observation model is given by re-parameterizing the unknown parameters. The step-by-step ambiguity fixing method, i.e. the extra-wide-lane, wide-lane and narrow-lane ambiguities fixed in sequence, is deduced by using double-differenced ambiguities in a network. With the GPS ⅡF, BDS-2 and Galileo being able to transmit triple-frequency signals, the four POD tests are conducted: ionospheric-free (IF) POD of frequency 1/2, IF POD of frequency 1/3, UC POD of frequency 1/2, UC POD of TF signals. The three metrics of external orbit product, day boundary discontinuities and satellite laser ranging are used to validate the POD product accuracy. Results show that a subtle improvement are received with the addition of the third frequency observations. However, the improvement of GPS TF POD results with respect to L1/L2 POD is about 10%, which may be the signal power of L5 is stronger than that of L2.
    Accuracy evaluation and improvement strategies of BeiDou broadcast clock error model
    GONG Xiuqiang, YUAN Junjun, HU Xiaogong, WANG Bin, CHEN Junping, ZHOU Shanshi
    2021, 50(2):  181-188.  doi:10.11947/j.AGCS.2021.20190411
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    The spaceborne atomic clock is the core equipment of Beidou Satellite Navigation System (BDS). Clock error model is an important part of BDS broadcast messages, and its accuracy directly affects the service performance of BDS. In this paper, the accuracy of the BDS clock model is evaluated using the TWSTF clock error measurement as a reference. The results show that the accuracy of the BDS clock model is better than 2 ns (data age less than 12 h). Aiming at the problem of calculating the clock error parameters of BDS, combined with the clock error model solution strategy of the BDS ground segment, this paper proposes a variety of strategies to further improve the accuracy. For the short-term forecast of 1 h duration, this paper proposes a clock error forecast method of weighted linear model and mixed interval linear model. The accuracy of short-term forecast can be increased by more than 20%. For medium and long-term (>6 h), clock error time series is analyzed by spectral method and prediction model is constructed according to the spectral analysis results. Compared with simple quadratic polynomial model, the prediction accuracy of the clock error model is improved by 13% in 6 h and 21% in 12 h on average. For IGSO/MEO satellite, this paper proposes a strategy of using TWSTF equipment delay as parameters to estimate, which improves the accuracy of clock error model by 18% after equipment switching. Finally, using the improvement strategy proposed in this paper, the clock error data from January to June 2017 is reprocessed to obtain a new broadcast clock error model time series. Using the new broadcast clock error model to locate the BDS monitoring receiver, the results show that, the direction accuracy of N, E and U is increased by 14.22%, 29.39% and 14.91% respectively, which significantly improves the service performance of BDS broadcast clock error model.
    Photogrammetry and Remote Sensing
    Deep learning algorithm for feature matching of cross modality remote sensing images
    LAN Chaozhen, LU Wanjie, YU Junming, XU Qing
    2021, 50(2):  189-202.  doi:10.11947/j.AGCS.2021.20200048
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    Focusing on the problem of difficulty in matching due to the differences in imaging modality, time phases, and resolutions of cross modality remote sensing images, a new deep learning feature matching method named CMM-Net is proposed. First, a convolutional neural network is used to extract high-dimensional feature maps of the cross modality remote sensing images. The key points are selected according to the conditions that both the channel maximum and local maximum are met, and the 512-dimensional descriptors in corresponding location are extracted on the feature map to complete the feature extraction. In the matching stage, after completing the fast-nearest neighbor searching, in order to solve the problem of lots of mismatched points, a purification algorithm with dynamic adaptive Euclidean distance and RANSAC constraints is proposed to ensure that the mismatches are effectively eliminated while retaining the correct matches. The algorithm was tested using multiple sets of cross modality remote sensing images and compared with other algorithms. The results show that the proposed algorithm has the ability to extract similar scale invariant features in cross modality images, and has strong adaptability and robustness.
    A ring detection method for levee features extraction based on airborne LiDAR data
    SHEN Dingtao, QIAN Tianlu, XIA Yu, CHEN Beiqing, ZHANG Yu, WANG Jiechen
    2021, 50(2):  203-214.  doi:10.11947/j.AGCS.2020.20200194
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    Extracting levee features (e.g. central lines, levee crowns, levee slopes and sections) has great significance for detecting demaged levees and security assessment. Airborne LiDAR can be utilized to produce continuous surface data DTM.Thus, it has its advantage of lighter workload and higher precision when extracting levee features. In this paper, it is proposed a method to extract levee features with airborne LiDAR-derived DTM. Firstly, central line of levees is generated by ring detection. Compared to conventional methods, ring detection is tolerant to deformated or broken central lines caued by levee erosion or collapsion. Secondly, on this basis, levee crown and slope data is producted according to the standard of levee slope classification. Lastly, equidistant section lines of levees are extracted from ring intersections, and automatically generated the levee section. An empirical research of our method was conducted on 120 km of levees in Gongshuangcha detention basin in the Dongting Lake area, China.This method performed well in accurate extraction of levee engineering information, and has great application potential.
    A LiDAR point cloud hierarchical semantic segmentation method combining CNN and MRF
    JIANG Tengping, WANG Yongjun, ZHANG Linqi, LIANG Chong, SUN Jian
    2021, 50(2):  215-225.  doi:10.11947/j.AGCS.2021.20200095
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    The result of point cloud semantic segmentation includes the recognition of multiple objects in the scene, which is an important part of 3D scene information extraction. It also plays a key role in many fields such as smart cities. Since the large amount of data and high scene complexity, however, most existing methods can only extract a limited type of objects with a relatively low recognition rate from laser scanning data. This paper proposes a hierarchical multiple object automatic extraction framework combining residual learning and Markov random field (MRF) optimization in a 3D LiDAR point cloud scene. The framework first filters raw data into ground points and non-ground points; buildings are extracted from non-ground points to reduce scene complexity; then the residual learning network is adopted to pointwise classify the remaining point clouds; finally, a MRF-based optimization is used for post-processing and improving the accuracy of point cloud classification. In order to evaluate the effectiveness and robustness of the proposed method, experiments were performed on three outdoor large-scale point cloud scenarios. Experimental results show that the proposed method can perform effective semantic segmentation on various types of point cloud scenes, with (94.6%, 96.8%, 95.7%), (88.5%, 90.5%, 89.2%) and (95.3%, 95.2%, 95.3%), respectively. In addition, compared with existing advanced methods, it is shown that our method significantly improves the performance of semantic segmentation.
    A self-adaptive regression algorithm with noise density function difference and its application to artificial target extraction
    JIA Xiangyang, HUANG Xianfeng, NIU Wenyuan, ZHANG Fan, GAO Yunlong, YANG Chong
    2021, 50(2):  226-234.  doi:10.11947/j.AGCS.2021.20190509
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    Instruments, surrounding environment and human operation often cause a lot of noise in the LiDAR, resulting in low model regression accuracy. RANSAC algorithm is widely used to solve model regression problems by virtue of its simple implementation and robustness. However, for different scenarios, RANSAC algorithm needs to constantly adjust the parameters to estimate the optimal model solution. Considering the RANSAC algorithm and its family existing shortcomings, according to the difference of density distribution between inliers and noise. This paper firstly optimizes the initial hypothesis model by using density weighted guided sampling, and then proposes a spatial density function to evaluate the optimal model and to calculate the number of iterations by using the spatial density function. The whole process does not need any prior knowledge. The method proposed in this paper can solve the model regression problem where the inliers ratio is more than 10%. In addition, compared with the existing methods, the method proposed in this paper can achieve high accuracy and robustness without prior information.
    Multi-feature fusion and random multi-graph synthetic building change method
    WANG Chang, ZHANG Yongsheng, JI Song, ZHANG Lei
    2021, 50(2):  235-247.  doi:10.11947/j.AGCS.2021.20200097
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    Change detection of building with remotely sensed image is a challenge work, as it has many issues, e.g. the approach for calculating difference image (DI) which could highlights the changes is not ideal, poor strategies for the training sample collection and low classification accuracy as well. This study analyzed the process from three aspects, namely DI construction, sample selection reliability, and classification method selection, and proposed a remote sensing image building change detection method based on multi-feature fusion and random multi-graphs. First, the spectral and textural features (gray level co-occurrence matrix), morphological building index features, and shape features (after optimum scale segmentation) of multi-temporal, multi-source remote sensing images were extracted. The spectral and textural DI features obtained using change vector analysis, morphological building index DI, and shape feature DI obtained by the subtraction method, were fused to construct the final DI which effectively highlighted the building change information. Second, we obtained the DI saliency map using the frequency-domain significance method. The coarse change detection map was derived by selecting pre-classified thresholds for the DI saliency map (changed pixels “buildings”, unchanged pixels, undetermined pixels) using the fuzzy c-means clustering algorithm to obtain high-quality building and non-building training samples. Finally, the neighborhood features of the non-building and the building were extracted from the remote sensing and feature images, and these were used as the training sample for random multiple training. Subsequently, this trained random multiple classification model was used to perform change detection on the coarse change detection map, resulting in the final change detection map. To verify the efficiency of the proposed method, homogeneous and heterogeneous images were selected for experimental analysis. The results showed that the proposed method could detect more building change information than other methods, and the Com, Cor, and FM values were significantly higher than those of other methods.
    DCLS-GAN: cloud removal method for plateau area of TH-1 satellite image
    ZHENG Kai, LI Jiansheng, WANG Junqiang, OUYANG Wen, GU Youyi, ZHANG Xun
    2021, 50(2):  248-259.  doi:10.11947/j.AGCS.2021.20200020
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    It has been a research hotspot to apply deep learning to remove cloud on satellite images. In this paper, we propose a cloud removal method based on DCLS-GAN for the plateau image of TH-1 satellite. The generator is constructed with the structure of encoder-decoder, and two types of fixed and removable cloud masks are used in training. The least squarereconstruction loss and cross-entropy adversarial loss are used to generate the prediction image of cloud coverage area, whilel east square loss is also used in the discriminator to identify the authenticity of the generated image. Joint optimization of generator and discriminator is achieved by continuous iteration, after which, bilinear interpolation is used to improve the restoration accuracy of cloud coverage area, and Poisson editing is used to smooth the prediction boundary and reduce the influence of artifacts. The experimental results on the testing dataset show that the cloud removal effect of proposed method exceeds classical methods and the original Context Encoder in peak signal-to-noise ratio and structure similarity, and experiments on images with real cloud area also show that proposed method has lower indicators under blind image quality assessment. Finally,the speed is faster than classical methods and equals Context Encoder, thus it has a better practical application prospect.
    Marine Survey
    The transfer learning with convolutional neural network method of side-scan sonar to identify wreck images
    TANG Yulin, JIN Shaohua, BIAN Gang, ZHANG Yonghou, LI Fan
    2021, 50(2):  260-269.  doi:10.11947/j.AGCS.2021.20200187
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    The Side-scan sonar image automatic recognition is an important part of verification for underwater obstacle and wreck search and rescue, in view of the traditional artificial interpretation of side-scan sonar image is inefficient, time consuming and resource consumption and strong subjective uncertainty and excessive reliance on experience. This paper attempts to introduce the method of convolutional neural network, considering that the side-scan sonar shipwreck image belongs to a small sample data set, and an automatic recognition method of side-scan sonar shipwreck image based on transfer learning is proposed.The sample data were expanded by means of normalization and image enhancement, the training set and testing set were divided into 4∶1 proportions, and an improved model was designed according to the characteristics of the side-scan sonar wreck data set by referring to the classical VGG-16 model, then, the improved model trained on the ImageNet image data set is used to learn and experiment on the small sample side-scan sonar shipwreck data set using two transfer learning methods: freeze and train and fine-tuning, and compared with new learning. The results show that the accuracy of the three methods for the recognition of side-scan sonar shipwreck images is 93.71%, 84.49% and 90.58%, respectively. The first transfer learning method has the highest accuracy rate, the fastest model convergence speed, and the highest AP value 92.45%, which is 8.06% and 3.06% higher than the second transfer learning and the new learning method, respectively,and has a better effect in improving the model’s recognition ability and training efficiency. which verifies the effectiveness and feasibility of this method and has certain practical guiding significance.
    Comparative analysis of airborne laser bathymetric waveforms denoising algorithms
    SONG Yue, LI Houpu, ZHAI Guojun
    2021, 50(2):  270-278.  doi:10.11947/j.AGCS.2021.20200094
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    Denoising fitting of airborne laser bathymetry data is a key step in extracting the bottom terrain. The algorithm effects of wavelet adaptive threshold denoising, empirical model denoising (EMD) and joint denoising are compared in this paper, and then multivariate Gaussian fitting is used to test the denoising effect. The optimal denoising algorithm and parameter selection are obtained by comparison, and it is realized that the high-precision extraction of seabed features. This study has shown that: when the sounding data is denoised by wavelet threshold, the fixed threshold wavelet denoising effect is superior to other denoising effects, and the denoising decomposition level is more than 6 layers, which tends to be stable. The average accuracy of the algorithm reaches 8.218 2 after the fifth-order Gaussian fitting of the denoising data. The algorithm has strong robustness, it can meet the technical requirements of blue-green laser practical application, and provides a reference for accurately extracting seafloor feature information.
    Summary of PhD Thesis
    Physical mechanism of non-linear signals driven by temperature variations in GPS position time series
    WANG Kaihua
    2021, 50(2):  279-279.  doi:10.11947/j.AGCS.2021.20190498
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    Monitoring coastal wetlands changes using fusion of high-resolution SAR and optical images
    WU Ruijuan
    2021, 50(2):  280-280.  doi:10.11947/j.AGCS.2021.20190504
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    Analysis on characteristics of dust elements and causes of heavy metal pollution in Beijing
    XIONG Qiulin
    2021, 50(2):  281-281.  doi:10.11947/j.AGCS.2021.20190510
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    Research on precise processing of side scan sonar image and object recognition methods
    WANG Xiao
    2021, 50(2):  282-282.  doi:10.11947/j.AGCS.2021.20190517
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    Research on associating un-correlated tracks based-on only very short arc angle observations of space debris
    LEI Xiangxu
    2021, 50(2):  283-283.  doi:10.11947/j.AGCS.2021.20190527
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    Research on airborne gravity survey technology and application
    LIU Zhanke
    2021, 50(2):  284-284.  doi:10.11947/j.AGCS.2021.20200513
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