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Fengxiang Jin
College of Surveying and Geo-Informatics, Shandong Jianzhu University, 105835 Jinan, Shandong, China

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Journal article
Published: 16 August 2021 in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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Discontinuity play an essential and pivotal role in the deformation monitoring and stability analysis of the rock mass, but there are still many challenges for accurately and rapidly extracting discontinuity. In this study, an extraction and characterization method of discontinuity sets based on point cloud supervoxel segmentation was proposed, which consists of four parts: (1) a multiresolution supervoxel segmentation (MRSS) algorithm was developed to classify unstructured point cloud into multiresolution facets and discrete points; (2) to extract the individual discontinuity, the single supervoxel that having spatial connectivity, similar planarity and parallelism was clustered; (3) the orientation of individual discontinuity were calculated respectively based on the plane fitting parameters; (4) for comprehensively analyzing the stability of rock mass, the improving k-means clustering algorithm is utilized to constructing the discontinuity sets that having similar orientation information. The novel method has been successfully tested on two practical cases (a rock cut and a side slope point cloud captured by the static terrestrial laser scanner). A comparison with existing methods shows that the deviation of the discontinuity orientation for rock cut is less than 1, and the time efficiency is increased by 2.6 times. In addition, the orientation variation of the seven principle discontinuity in the five temporal side slope point cloud is relatively small, the dip direction and angle are within 2 and 1, respectively. We can conclude that the proposed method can efficiently obtain the full extent of every individual discontinuity from rock mass surface point cloud and accurately analyze orientation.

ACS Style

Wenxiao Sun; Jian Wang; Yikun Yang; Fengxiang Jin. Rock Mass Discontinuity Extraction Method Based on Multiresolution Supervoxel Segmentation of Point Cloud. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2021, PP, 1 -1.

AMA Style

Wenxiao Sun, Jian Wang, Yikun Yang, Fengxiang Jin. Rock Mass Discontinuity Extraction Method Based on Multiresolution Supervoxel Segmentation of Point Cloud. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2021; PP (99):1-1.

Chicago/Turabian Style

Wenxiao Sun; Jian Wang; Yikun Yang; Fengxiang Jin. 2021. "Rock Mass Discontinuity Extraction Method Based on Multiresolution Supervoxel Segmentation of Point Cloud." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing PP, no. 99: 1-1.

Journal article
Published: 01 March 2021 in ISPRS International Journal of Geo-Information
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As air users, the public is also participants in air pollution control and important evaluators of environmental protection. Therefore, understanding the public perception and response to air pollution is an essential part of improving air governance. This study proposed an analytical framework for public response to air pollution based on online complaint data and sentiment analysis. In the proposed framework, the emotional dictionary of air pollution was firstly constructed using microblog data and complaint data. Secondly, the emotional dictionary of air pollution and the sentiment analysis method were used to calculate public complaints’ emotional intensity. Besides, the spatial and temporal characteristics of air pollution complaint data and public emotional intensity, the complaints content, and their correlation with PM2.5 (particulate matters smaller than 2.5 micrometers) and PM10 were analyzed using address matching, spatial analysis, and word cloud analysis. Finally, the proposed framework was applied to 13,469 air pollution complaint data in Shandong Province from 2012 to 2018. The obtained results indicated that: the public was mainly complaining about the exhaust gas emissions from enterprises and factories. Spatially, the geographical center of complaint data was located in the inland industrial urban agglomeration of Shandong Province. Correlatively, air pollution complaints’ negative emotional intensity was significantly negatively correlated with PM2.5 (−0.73). Moreover, the number of public complaints about air pollution and the intensity of negative emotions also decreased with improved air quality in Shandong Province in recent years.

ACS Style

Yong Sun; Min Ji; Fengxiang Jin; Huimeng Wang. Public Responses to Air Pollution in Shandong Province Using the Online Complaint Data. ISPRS International Journal of Geo-Information 2021, 10, 126 .

AMA Style

Yong Sun, Min Ji, Fengxiang Jin, Huimeng Wang. Public Responses to Air Pollution in Shandong Province Using the Online Complaint Data. ISPRS International Journal of Geo-Information. 2021; 10 (3):126.

Chicago/Turabian Style

Yong Sun; Min Ji; Fengxiang Jin; Huimeng Wang. 2021. "Public Responses to Air Pollution in Shandong Province Using the Online Complaint Data." ISPRS International Journal of Geo-Information 10, no. 3: 126.

Journal article
Published: 21 February 2021 in Applied Sciences
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Severe air pollution problems have led to a rise in the Chinese public’s concern, and it is necessary to use monitoring stations to monitor and evaluate pollutant levels. However, monitoring stations are limited, and the public is everywhere. It is also essential to understand the public’s awareness and behavioral response to air pollution. Air pollution complaint data can more directly reflect the public’s real air quality perception than social media data. Therefore, based on air pollution complaint data and sentiment analysis, we proposed a new air pollution perception index (APPI) in this paper. Firstly, we constructed the emotional dictionary for air pollution and used sentiment analysis to calculate public complaints’ emotional intensity. Secondly, we used the piecewise function to obtain the APPI based on the complaint Kernel density and complaint emotion Kriging interpolation, and we further analyzed the change of center of gravity of the APPI. Finally, we used the proposed APPI to examine the 2012 to 2017 air pollution complaint data in Shandong Province, China. The results were verified by the POI (points of interest) data and word cloud analysis. The results show that: (1) the statistical analysis and spatial distribution of air pollution complaint density and public complaint emotion intensity are not entirely consistent. The proposed APPI can more reasonably evaluate the public perception of air pollution. (2) The public perception of air pollution tends to the southwest of Shandong Province, while coastal cities are relatively weak. (3) The content of public complaints about air pollution mainly focuses on the exhaust emissions of enterprises. Moreover, the more enterprises gather in inland cities, the public perception of air pollution is stronger.

ACS Style

Yong Sun; Fengxiang Jin; Yan Zheng; Min Ji; Huimeng Wang. A New Indicator to Assess Public Perception of Air Pollution Based on Complaint Data. Applied Sciences 2021, 11, 1894 .

AMA Style

Yong Sun, Fengxiang Jin, Yan Zheng, Min Ji, Huimeng Wang. A New Indicator to Assess Public Perception of Air Pollution Based on Complaint Data. Applied Sciences. 2021; 11 (4):1894.

Chicago/Turabian Style

Yong Sun; Fengxiang Jin; Yan Zheng; Min Ji; Huimeng Wang. 2021. "A New Indicator to Assess Public Perception of Air Pollution Based on Complaint Data." Applied Sciences 11, no. 4: 1894.

Journal article
Published: 14 January 2021 in Remote Sensing
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The available stereo matching algorithms produce large number of false positive matches or only produce a few true-positives across oblique stereo images with large baseline. This undesired result happens due to the complex perspective deformation and radiometric distortion across the images. To address this problem, we propose a novel affine invariant feature matching algorithm with subpixel accuracy based on an end-to-end convolutional neural network (CNN). In our method, we adopt and modify a Hessian affine network, which we refer to as IHesAffNet, to obtain affine invariant Hessian regions using deep learning framework. To improve the correlation between corresponding features, we introduce an empirical weighted loss function (EWLF) based on the negative samples using K nearest neighbors, and then generate deep learning-based descriptors with high discrimination that is realized with our multiple hard network structure (MTHardNets). Following this step, the conjugate features are produced by using the Euclidean distance ratio as the matching metric, and the accuracy of matches are optimized through the deep learning transform based least square matching (DLT-LSM). Finally, experiments on Large baseline oblique stereo images acquired by ground close-range and unmanned aerial vehicle (UAV) verify the effectiveness of the proposed approach, and comprehensive comparisons demonstrate that our matching algorithm outperforms the state-of-art methods in terms of accuracy, distribution and correct ratio. The main contributions of this article are: (i) our proposed MTHardNets can generate high quality descriptors; and (ii) the IHesAffNet can produce substantial affine invariant corresponding features with reliable transform parameters.

ACS Style

Guobiao Yao; Alper Yilmaz; Li Zhang; Fei Meng; Haibin Ai; Fengxiang Jin. Matching Large Baseline Oblique Stereo Images Using an End-To-End Convolutional Neural Network. Remote Sensing 2021, 13, 274 .

AMA Style

Guobiao Yao, Alper Yilmaz, Li Zhang, Fei Meng, Haibin Ai, Fengxiang Jin. Matching Large Baseline Oblique Stereo Images Using an End-To-End Convolutional Neural Network. Remote Sensing. 2021; 13 (2):274.

Chicago/Turabian Style

Guobiao Yao; Alper Yilmaz; Li Zhang; Fei Meng; Haibin Ai; Fengxiang Jin. 2021. "Matching Large Baseline Oblique Stereo Images Using an End-To-End Convolutional Neural Network." Remote Sensing 13, no. 2: 274.

Journal article
Published: 01 February 2019 in Remote Sensing
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This paper presents a novel framework to extract metro tunnel cross sections (profiles) from Terrestrial Laser Scanning point clouds. The entire framework consists of two steps: tunnel central axis extraction and cross section determination. In tunnel central extraction, we propose a slice-based method to obtain an initial central axis, which is further divided into linear and nonlinear circular segments by an enhanced Random Sample Consensus (RANSAC) tunnel axis segmentation algorithm. This algorithm transforms the problem of hybrid linear and nonlinear segment extraction into a sole segmentation of linear elements defined at the tangent space rather than raw data space, significantly simplifying the tunnel axis segmentation. The extracted axis segments are then provided as input to the step of the cross section determination which generates the coarse cross-sectional points by intersecting a series of straight lines that rotate orthogonally around the tunnel axis with their local fitted quadric surface, i.e., cylindrical surface. These generated profile points are further refined and densified via solving a constrained nonlinear least squares problem. Our experiments on Nanjing metro tunnel show that the cross sectional fitting error is only 1.69 mm. Compared with the designed radius of the metro tunnel, the RMSE (Root Mean Square Error) of extracted cross sections’ radii only keeps 1.60 mm. We also test our algorithm on another metro tunnel in Shanghai, and the results show that the RMSE of radii only keeps 4.60 mm which is superior to a state-of-the-art method of 6.00 mm. Apart from the accurate geometry, our approach can maintain the correct topology among cross sections, thereby guaranteeing the production of geometric tunnel model without crack defects. Moreover, we prove that our algorithm is insensitive to the missing data and point density.

ACS Style

Zhen Cao; Dong Chen; Yufeng Shi; Zhenxin Zhang; Fengxiang Jin; Ting Yun; Sheng Xu; Zhizhong Kang; Liqiang Zhang. A Flexible Architecture for Extracting Metro Tunnel Cross Sections from Terrestrial Laser Scanning Point Clouds. Remote Sensing 2019, 11, 297 .

AMA Style

Zhen Cao, Dong Chen, Yufeng Shi, Zhenxin Zhang, Fengxiang Jin, Ting Yun, Sheng Xu, Zhizhong Kang, Liqiang Zhang. A Flexible Architecture for Extracting Metro Tunnel Cross Sections from Terrestrial Laser Scanning Point Clouds. Remote Sensing. 2019; 11 (3):297.

Chicago/Turabian Style

Zhen Cao; Dong Chen; Yufeng Shi; Zhenxin Zhang; Fengxiang Jin; Ting Yun; Sheng Xu; Zhizhong Kang; Liqiang Zhang. 2019. "A Flexible Architecture for Extracting Metro Tunnel Cross Sections from Terrestrial Laser Scanning Point Clouds." Remote Sensing 11, no. 3: 297.