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Qingquan Li
College of Civil and Transportation Engineering, Shenzhen University, Shenzhen 518060, China

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Journal article
Published: 05 August 2021 in Remote Sensing
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Ground deformation related to mining activities may occur immediately or many years later, leading to a series of mine geological disasters, such as ground fissures, collapses, and even mining earthquakes. Deformation monitoring has been carried out with techniques, such as multitemporal interferometric synthetic aperture radar (MTInSAR). Over the past decade, MTInSAR has been widely used in monitoring mining deformation, and it is still difficult to retrieve mining deformation over dense vegetation areas. In this study, we use multiple-platform SAR images to retrieve mining deformation over dense vegetation areas. The high-quality interferograms are selected by the coherence map, and the mining deformation is retrieved by the MSBAS-InSAR technique. SAR images from TerraSAR-X, Sentinel-1A, Radarsat-2, and PALSAR-2 over the Fengfeng mining area, Heibei, China, are used to retrieve the deformation of mining activities covered with dense vegetation. The results show that the subsidence in the Fengfeng mining area reaches up to 90 cm over the period from July 2015 to April 2016. The root-mean-square error (RMSE) between the results from InSAR and leveling is 83.5 mm/yr at two mining sites, i.e., Wannian and Jiulong Mines.

ACS Style

Bochen Zhang; Songbo Wu; Xiaoli Ding; Chisheng Wang; Jiasong Zhu; Qingquan Li. Use of Multiplatform SAR Imagery in Mining Deformation Monitoring with Dense Vegetation Coverage: A Case Study in the Fengfeng Mining Area, China. Remote Sensing 2021, 13, 3091 .

AMA Style

Bochen Zhang, Songbo Wu, Xiaoli Ding, Chisheng Wang, Jiasong Zhu, Qingquan Li. Use of Multiplatform SAR Imagery in Mining Deformation Monitoring with Dense Vegetation Coverage: A Case Study in the Fengfeng Mining Area, China. Remote Sensing. 2021; 13 (16):3091.

Chicago/Turabian Style

Bochen Zhang; Songbo Wu; Xiaoli Ding; Chisheng Wang; Jiasong Zhu; Qingquan Li. 2021. "Use of Multiplatform SAR Imagery in Mining Deformation Monitoring with Dense Vegetation Coverage: A Case Study in the Fengfeng Mining Area, China." Remote Sensing 13, no. 16: 3091.

Journal article
Published: 27 July 2021 in International Journal of Applied Earth Observation and Geoinformation
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Urban agglomeration is the most obvious regions in the Chinese rapid urban land expansion. The developed urban agglomerations in China (i.e., Beijing-Tianjin-Hebei (BTH), Yangtze River Delta (YRD) and Guangdong–Hong Kong–Macau Greater Bay Area (GBA)) have entered a suburban urbanization period; however, it is not clear whether the urbanization on low-slope hilly regions (hillside urbanization) exist in these urban agglomerations. In this study, we proposed a quantification framework to detect hillside urbanization with multiple earth observation data and socio-economic data and further compared their spatiotemporal patterns from 2007 to 2017 in these three urban agglomerations. The results showed: (1) the urban area of BTH, YRD and GBA has expanded by 1.82, 2.37 and 1.53 times, respectively; (2) widespread hillside urbanization regions were found in BTH (475.82 km2), YRD (440.41 km2) and GBA (298.14 km2); (3) GBA had the largest hillside urbanization rate (10.55%), followed by BTH (6.33%) and YRD (3.18%); (4) the hillside urbanization of BTH, YRD and GBA provided accommodation and workplaces for about 1.05, 0.97 and 1.37 million people, respectively; and (5) the minimum and maximum high environmental cost (HEC) hillside urbanization rates were found in BTH (0.53%) and GBA (2.92%), respectively. Our findings may provide some new insights into urban sustainability.

ACS Style

Chao Yang; Rongling Xia; Qingquan Li; Huizeng Liu; Tiezhu Shi; Guofeng Wu. Comparing hillside urbanizations of Beijing-Tianjin-Hebei, Yangtze River Delta and Guangdong–Hong Kong–Macau greater Bay area urban agglomerations in China. International Journal of Applied Earth Observation and Geoinformation 2021, 102, 102460 .

AMA Style

Chao Yang, Rongling Xia, Qingquan Li, Huizeng Liu, Tiezhu Shi, Guofeng Wu. Comparing hillside urbanizations of Beijing-Tianjin-Hebei, Yangtze River Delta and Guangdong–Hong Kong–Macau greater Bay area urban agglomerations in China. International Journal of Applied Earth Observation and Geoinformation. 2021; 102 ():102460.

Chicago/Turabian Style

Chao Yang; Rongling Xia; Qingquan Li; Huizeng Liu; Tiezhu Shi; Guofeng Wu. 2021. "Comparing hillside urbanizations of Beijing-Tianjin-Hebei, Yangtze River Delta and Guangdong–Hong Kong–Macau greater Bay area urban agglomerations in China." International Journal of Applied Earth Observation and Geoinformation 102, no. : 102460.

Journal article
Published: 10 July 2021 in Remote Sensing
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LiDAR (light detection and ranging), as an active sensor, is investigated in the simultaneous localization and mapping (SLAM) system. Typically, a LiDAR SLAM system consists of front-end odometry and back-end optimization modules. Loop closure detection and pose graph optimization are the key factors determining the performance of the LiDAR SLAM system. However, the LiDAR works at a single wavelength (905 nm), and few textures or visual features are extracted, which restricts the performance of point clouds matching based loop closure detection and graph optimization. With the aim of improving LiDAR SLAM performance, in this paper, we proposed a LiDAR and visual SLAM backend, which utilizes LiDAR geometry features and visual features to accomplish loop closure detection. Firstly, the bag of word (BoW) model, describing the visual similarities, was constructed to assist in the loop closure detection and, secondly, point clouds re-matching was conducted to verify the loop closure detection and accomplish graph optimization. Experiments with different datasets were carried out for assessing the proposed method, and the results demonstrated that the inclusion of the visual features effectively helped with the loop closure detection and improved LiDAR SLAM performance. In addition, the source code, which is open source, is available for download once you contact the corresponding author.

ACS Style

Shoubin Chen; Baoding Zhou; Changhui Jiang; Weixing Xue; Qingquan Li. A LiDAR/Visual SLAM Backend with Loop Closure Detection and Graph Optimization. Remote Sensing 2021, 13, 2720 .

AMA Style

Shoubin Chen, Baoding Zhou, Changhui Jiang, Weixing Xue, Qingquan Li. A LiDAR/Visual SLAM Backend with Loop Closure Detection and Graph Optimization. Remote Sensing. 2021; 13 (14):2720.

Chicago/Turabian Style

Shoubin Chen; Baoding Zhou; Changhui Jiang; Weixing Xue; Qingquan Li. 2021. "A LiDAR/Visual SLAM Backend with Loop Closure Detection and Graph Optimization." Remote Sensing 13, no. 14: 2720.

Journal article
Published: 18 June 2021 in IEEE Transactions on Geoscience and Remote Sensing
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Due to the detailed spectral information through hundreds of narrow spectral bands provided by hyperspectral image (HSI) data, it can be employed to accurately classify diverse materials of interest, which is one of the core applications of hyperspectral remote sensing technology. In recent years, with the rapid development of deep learning, convolutional neural networks (CNNs) have been successfully applied in many fields, including HSI classification. However, the random gradient descent-based parameter updating scheme is too general and leading to the inefficiency of CNN models. Moreover, the high dimensionality and limited training samples of HSI data also exacerbate the overfitting problem. To tackle these issues, in this article, a novel deep network with multilayer and multibranch architecture, named 3-D Gabor CNN (3DG-CNN), is proposed for HSI classification. More precisely, since the predefined 3-D Gabor filters in multiple scales and orientations could well characterize the internal spatial-spectral structure of HSI data from various perspectives, the 3-D Gabor-modulated kernels (3-D GMKs) are employed to replace the random initialization kernels. Moreover, the specially designed multibranch architecture enables the network to better integrating the scalable property of 3-D Gabor filters; thus, the representative ability and robustness of the extracted features can be greatly improved. Alternatively, the number of network parameters is substantially reduced due to the incorporation of 3-D Gabor modulation, relieving the training complexity and also alleviating the training process from overfitting. Experimental results on four real HSI datasets (including two newly released ones in the literature) have demonstrated that the proposed 3DG-CNN model can achieve better performance than several widely used machine-learning-based and deep-learning-based approaches. For the sake of reproducibility, the codes of the proposed 3DG-CNN model are available at http://jiasen.tech/papers/.

ACS Style

Sen Jia; Jianhui Liao; Meng Xu; Yan Li; Jiasong Zhu; Weiwei Sun; Xiuping Jia; Qingquan Li. 3-D Gabor Convolutional Neural Network for Hyperspectral Image Classification. IEEE Transactions on Geoscience and Remote Sensing 2021, PP, 1 -16.

AMA Style

Sen Jia, Jianhui Liao, Meng Xu, Yan Li, Jiasong Zhu, Weiwei Sun, Xiuping Jia, Qingquan Li. 3-D Gabor Convolutional Neural Network for Hyperspectral Image Classification. IEEE Transactions on Geoscience and Remote Sensing. 2021; PP (99):1-16.

Chicago/Turabian Style

Sen Jia; Jianhui Liao; Meng Xu; Yan Li; Jiasong Zhu; Weiwei Sun; Xiuping Jia; Qingquan Li. 2021. "3-D Gabor Convolutional Neural Network for Hyperspectral Image Classification." IEEE Transactions on Geoscience and Remote Sensing PP, no. 99: 1-16.

Journal article
Published: 19 May 2021 in ISPRS Journal of Photogrammetry and Remote Sensing
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Indoor map is a fundamental element of indoor location-based services (ILBS). However, traditional indoor mapping techniques are labor-intensive and time-consuming. The advancement of smartphones offers great opportunities for crowdsourcing-based indoor mapping, which is one of the most promising applications due to its low cost and flexibility. Over the last decade, many crowdsourcing-based indoor mapping solutions using smartphones have been proposed. This article provides a systematic review of these works. Different from former surveys, we classify the indoor mapping process by the stage of map construction. In particular, we highlight the two key steps, geospatial-element acquisition, and indoor-map construction, and provide state-of-the-art techniques on these topics. Then, we systematically review the crowdsourcing-based indoor mapping solutions under grid-based, landmark-based, and semantic maps. In addition to covering the principles, benefits, and challenges, these systems are compared in terms of sensors, participation, output, experimental environment, and reported accuracy. Besides these existing performance criteria, we extract quantitative performance criteria that are suitable to evaluate crowdsourcing-based indoor mapping solutions. Finally, we present open issues and future research directions.

ACS Style

Baoding Zhou; Wei Ma; Qingquan Li; Naser El-Sheimy; Qingzhou Mao; You Li; Fuqiang Gu; Lian Huang; Jiasong Zhu. Crowdsourcing-based indoor mapping using smartphones: A survey. ISPRS Journal of Photogrammetry and Remote Sensing 2021, 177, 131 -146.

AMA Style

Baoding Zhou, Wei Ma, Qingquan Li, Naser El-Sheimy, Qingzhou Mao, You Li, Fuqiang Gu, Lian Huang, Jiasong Zhu. Crowdsourcing-based indoor mapping using smartphones: A survey. ISPRS Journal of Photogrammetry and Remote Sensing. 2021; 177 ():131-146.

Chicago/Turabian Style

Baoding Zhou; Wei Ma; Qingquan Li; Naser El-Sheimy; Qingzhou Mao; You Li; Fuqiang Gu; Lian Huang; Jiasong Zhu. 2021. "Crowdsourcing-based indoor mapping using smartphones: A survey." ISPRS Journal of Photogrammetry and Remote Sensing 177, no. : 131-146.

Journal article
Published: 07 May 2021 in Remote Sensing
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Understanding intra-urban travel patterns is beneficial for urban planning and transportation management, among other fields. As an emerging travel mode, online car-hailing platforms provide massive and high-precision trajectory data, thus offering new opportunities for gaining insights into human mobility. This paper aims to explore temporal intra-urban travel patterns by fitting the distributions of mobility metrics and leveraging the boxplot. The statistical characteristics of daily and hourly travel distance are relatively stable, while those of travel time and speed have some fluctuations. More specifically, most residents travel between 2 and 10 km, with travel times ranging from 6.6 to 30 min, which is fairly consistent with our daily experience. Mainly attributed to travel cost, individuals seldom use online car-hailing for too short or long trips. It is worth mentioning that a weekly pattern can be found in all mobility metrics, in which the patterns of travel time and speed are more obvious than that of travel distance. In addition, since October has more rainy days than November, travel distances and travel times in October are higher than that in November, while the opposite is true for travel speed. This paper can provide a beneficial reference for understanding temporal human mobility patterns, and lays a solid foundation for future research.

ACS Style

Chaoyang Shi; Qingquan Li; Shiwei Lu; Xiping Yang. Exploring Temporal Intra-Urban Travel Patterns: An Online Car-Hailing Trajectory Data Perspective. Remote Sensing 2021, 13, 1825 .

AMA Style

Chaoyang Shi, Qingquan Li, Shiwei Lu, Xiping Yang. Exploring Temporal Intra-Urban Travel Patterns: An Online Car-Hailing Trajectory Data Perspective. Remote Sensing. 2021; 13 (9):1825.

Chicago/Turabian Style

Chaoyang Shi; Qingquan Li; Shiwei Lu; Xiping Yang. 2021. "Exploring Temporal Intra-Urban Travel Patterns: An Online Car-Hailing Trajectory Data Perspective." Remote Sensing 13, no. 9: 1825.

Journal article
Published: 30 April 2021 in Remote Sensing
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Ocean waves are a vital environmental factor that affects the accuracy of airborne laser bathymetry (ALB) systems. As the regional water surface undulates with randomness, the laser propagation direction through the air–water surface will change and impact the underwater topographic result from the ALB system, especially for the small laser divergence system. However, the natural ocean surface changes rapidly over time, and uneven ocean surface point clouds from ALB scanning will cause an uncertain estimation of the laser propagation direction; therefore, a self-adaptive correction method based on the characteristics of the partial wave surface is key to improving the accuracy and applicability of the ALB system. In this paper, we focused on the issues of spatial position deviation caused by surface waves and position correction of the underwater laser footprint, and the dimension-based adaptive method is applied to attempt to correct the laser incidence angle. Simulation experiments and analysis of the actual measurement data from different ALB systems verified that the method can effectively suppress the influence of ocean waves. Furthermore, the inversion result of sea surface inclination changes is consistent with the surface wind wave reanalysis products. Based on the laser underwater propagation model in the strategy, we also quantitatively analyzed the influence of surface waves on laser bathymetry, which can guide the operation selection and data processing of the ALB system at specific water depths and under dynamic ocean conditions.

ACS Style

Kai Guo; Qingquan Li; Qingzhou Mao; Chisheng Wang; Jiasong Zhu; Yanxiong Liu; Wenxue Xu; Dejin Zhang; Anlei Wu. Errors of Airborne Bathymetry LiDAR Detection Caused by Ocean Waves and Dimension-Based Laser Incidence Correction. Remote Sensing 2021, 13, 1750 .

AMA Style

Kai Guo, Qingquan Li, Qingzhou Mao, Chisheng Wang, Jiasong Zhu, Yanxiong Liu, Wenxue Xu, Dejin Zhang, Anlei Wu. Errors of Airborne Bathymetry LiDAR Detection Caused by Ocean Waves and Dimension-Based Laser Incidence Correction. Remote Sensing. 2021; 13 (9):1750.

Chicago/Turabian Style

Kai Guo; Qingquan Li; Qingzhou Mao; Chisheng Wang; Jiasong Zhu; Yanxiong Liu; Wenxue Xu; Dejin Zhang; Anlei Wu. 2021. "Errors of Airborne Bathymetry LiDAR Detection Caused by Ocean Waves and Dimension-Based Laser Incidence Correction." Remote Sensing 13, no. 9: 1750.

Journal article
Published: 28 April 2021 in IEEE Transactions on Geoscience and Remote Sensing
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Domain adaptation, which cleverly applies the classifier learned from the source domain with sufficient labeled samples to the target domain with limited labeled samples, provides a feasible alternative to handle the small training sample problem of hyperspectral image (HSI) classification and has attracted much attention in the research field recently. Apparently, feature discriminative ability is vital for domain adaptation, which plays a crucial role during the migration process of transfer learning. In this article, a gradient feature-oriented 3-D domain adaptation (GF-3DDA) approach is proposed for HSI classification. First, 3-D Gabor is employed to remove noise from the original data, and two 2-D gradient-based features, 2-D Sobel gradient (SG) and 2-D derivative-of-Gaussian (DtG), are extended to the 3-D domain to coincide with the integrated spatial-spectral organization of HSI. Thus, the 3-D Sobel-Gabor gradient (3DSGG) and 3-D derivative-of-Gaussian-Gabor (3DDGG) features are achieved. Second, a 3-D domain adaptation method is implemented to jointly exploit the second- and fourth-order statistical descriptors in the spatial-spectral dimensions, which could effectively reduce domain shifts and thus achieve improved domain adaptation. Third, all the extracted domain-adapted feature modules are collaboratively classified by extreme learning machine (ELM), and the probability-like outputs of every ELM classifier are combined together to accomplish the classification task. Four hyperspectral data sets that each contains two scenes, i.e., Pavia, Shanghai-Hangzhou, Indiana, and Houston, are tested in the experiments. When only ten labeled samples per class are used in the target domain, the classification accuracies on four hyperspectral data sets achieved by our GF-3DDA approach are 93.31%, 84.35%, 69.32%, and 80.06%, respectively.

ACS Style

Sen Jia; Xiaomei Liu; Meng Xu; Qiao Yan; Jun Zhou; Xiuping Jia; Qingquan Li. Gradient Feature-Oriented 3-D Domain Adaptation for Hyperspectral Image Classification. IEEE Transactions on Geoscience and Remote Sensing 2021, PP, 1 -17.

AMA Style

Sen Jia, Xiaomei Liu, Meng Xu, Qiao Yan, Jun Zhou, Xiuping Jia, Qingquan Li. Gradient Feature-Oriented 3-D Domain Adaptation for Hyperspectral Image Classification. IEEE Transactions on Geoscience and Remote Sensing. 2021; PP (99):1-17.

Chicago/Turabian Style

Sen Jia; Xiaomei Liu; Meng Xu; Qiao Yan; Jun Zhou; Xiuping Jia; Qingquan Li. 2021. "Gradient Feature-Oriented 3-D Domain Adaptation for Hyperspectral Image Classification." IEEE Transactions on Geoscience and Remote Sensing PP, no. 99: 1-17.

Journal article
Published: 17 April 2021 in ISPRS International Journal of Geo-Information
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Modeling the distribution of daily and hourly human mobility metrics is beneficial for studying underlying human travel patterns. In previous studies, some probability distribution functions were employed in order to establish a base for human mobility research. However, the selection of the most suitable distribution is still a challenging task. In this paper, we focus on modeling the distributions of travel distance, travel time, and travel speed. The daily and hourly trip data are fitted with several candidate distributions, and the best one is selected based on the Bayesian information criterion. A case study with online car-hailing data in Xi’an, China, is presented to demonstrate and evaluate the model fit. The results indicate that travel distance and travel time of daily and hourly human mobility tend to follow Gamma distribution, and travel speed can be approximated by Burr distribution. These results can contribute to a better understanding of online car-hailing travel patterns and establish a base for human mobility research.

ACS Style

Chaoyang Shi; Qingquan Li; Shiwei Lu; Xiping Yang. Modeling the Distribution of Human Mobility Metrics with Online Car-Hailing Data—An Empirical Study in Xi’an, China. ISPRS International Journal of Geo-Information 2021, 10, 268 .

AMA Style

Chaoyang Shi, Qingquan Li, Shiwei Lu, Xiping Yang. Modeling the Distribution of Human Mobility Metrics with Online Car-Hailing Data—An Empirical Study in Xi’an, China. ISPRS International Journal of Geo-Information. 2021; 10 (4):268.

Chicago/Turabian Style

Chaoyang Shi; Qingquan Li; Shiwei Lu; Xiping Yang. 2021. "Modeling the Distribution of Human Mobility Metrics with Online Car-Hailing Data—An Empirical Study in Xi’an, China." ISPRS International Journal of Geo-Information 10, no. 4: 268.

Chapter
Published: 07 April 2021 in The Life and Afterlife of Gay Neighborhoods
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The emergence of Web 2.0 and mobile Internet produces massive user-generated content (UGC), including geo-tagged photos, social network posts, street view images, and crowdsourced GPS trajectories. UGC creates unprecedented opportunities to sense what was previously hidden in the physical surfaces of cities and to portray the interactions of infrastructures, geo-information, and people; therefore, it is not only a new lens for urban space but also leads to innovative applications. In this chapter, we will introduce several typical types of UGC, such as geo-tagged photos, social media data, crowdsourcing GPS trajectories, and videos. We showcase ways in which user-generated big data can be harvested and analyzed to generate invisible and impressionistic landscapes of urban dynamics and to stimulate innovative applications. We discuss typical UGC-driven applications to demonstrate the potential of UGC in revealing how urban spaces are perceived by the public, establishing links between tangible artifacts and physical-cyber-social spaces. This fosters alternative approaches to urban informatics that better capture the intricate nature of urban space and its dynamics.

ACS Style

Wei Tu; Qingquan Li; Yatao Zhang; Yang Yue. User-Generated Content and Its Applications in Urban Studies. The Life and Afterlife of Gay Neighborhoods 2021, 523 -539.

AMA Style

Wei Tu, Qingquan Li, Yatao Zhang, Yang Yue. User-Generated Content and Its Applications in Urban Studies. The Life and Afterlife of Gay Neighborhoods. 2021; ():523-539.

Chicago/Turabian Style

Wei Tu; Qingquan Li; Yatao Zhang; Yang Yue. 2021. "User-Generated Content and Its Applications in Urban Studies." The Life and Afterlife of Gay Neighborhoods , no. : 523-539.

Journal article
Published: 18 March 2021 in IEEE Transactions on Intelligent Transportation Systems
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Simultaneous localization and mapping (SLAM) is a fundamental technique block in the indoor-navigation system for most autonomous vehicles and robots. SLAM aims at building a global consistent map of the environment while simultaneously determining the position and orientation of the robot in this map. Significant advances have been made in visual SLAM techniques in the past several years. However, due to the fragile performance in tracking feature points in environments that lack texture, e.g., a warehouse with blank white walls, visual SLAM can hardly provide a reliable localization. Compared with visual SLAM, LiDAR SLAM can often provide more robust localization in indoor environments by using 3D spatial information directly captured by LiDAR point clouds. Thus, LiDAR SLAM techniques are often employed in industrial applications such as automated guided vehicles (AGVs). In the past decades, a number of LiDAR SLAM methods have been proposed. However, the strength and weakness points of various LiDAR SLAMs are not clear, which may perplex the researchers and engineers. In this article, analysis and comparisons are made on different LiDAR SLAM-based indoor navigation methods, and extensive experiments are conducted to evaluate their performances in real environments. The comparative analysis and results can help researchers in academia and industry in constructing a suitable LiDAR SLAM system for indoor navigation for their own usage scenarios.

ACS Style

Qin Zou; Qin Sun; Long Chen; Bu Nie; Qingquan Li. A Comparative Analysis of LiDAR SLAM-Based Indoor Navigation for Autonomous Vehicles. IEEE Transactions on Intelligent Transportation Systems 2021, PP, 1 -15.

AMA Style

Qin Zou, Qin Sun, Long Chen, Bu Nie, Qingquan Li. A Comparative Analysis of LiDAR SLAM-Based Indoor Navigation for Autonomous Vehicles. IEEE Transactions on Intelligent Transportation Systems. 2021; PP (99):1-15.

Chicago/Turabian Style

Qin Zou; Qin Sun; Long Chen; Bu Nie; Qingquan Li. 2021. "A Comparative Analysis of LiDAR SLAM-Based Indoor Navigation for Autonomous Vehicles." IEEE Transactions on Intelligent Transportation Systems PP, no. 99: 1-15.

Journal article
Published: 03 February 2021 in Remote Sensing
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The synthetic aperture radar interferometry (InSAR) technique has been applied in monitoring the deformation of infrastructures, such as bridges, highways, railways and subways. Persistent scatterer (PS)-InSAR is one of the InSAR techniques, which utilises persistent scatterers to derive long-term displacements. This study applied time-series methods to post-process the PS-InSAR-derived time-series displacements with the use of 86 Sentinel-1A acquisitions spanning from 6 January 2018 to 27 November 2020. Empirical mode decomposition (EMD) and seasonal and trend decomposition using loess (STL) were combined to estimate the seasonal component of the total time-series displacements. Then, a temperature correlation map was generated by correlating the seasonal component with the temperature variation. Results show that the thermal expansion phenomenon is pronounced on the buildings of the Zhuhai–Macao Passenger Terminal as well as the bridge and road connecting to the Hong Kong International Airport (HKIA), while it is less obviously observed at the main Hong Kong-Zhuhai-Macao Bridge (HZMB). In addition, sudden changes between subsidence and uplift can be detected through the p-values derived by applying the augmented Dickey-Fuller (ADF) test to the residual signals after removing the linear and seasonal components from the original ones.

ACS Style

Siting Xiong; Chisheng Wang; Xiaoqiong Qin; Bochen Zhang; Qingquan Li. Time-Series Analysis on Persistent Scatter-Interferometric Synthetic Aperture Radar (PS-InSAR) Derived Displacements of the Hong Kong–Zhuhai–Macao Bridge (HZMB) from Sentinel-1A Observations. Remote Sensing 2021, 13, 546 .

AMA Style

Siting Xiong, Chisheng Wang, Xiaoqiong Qin, Bochen Zhang, Qingquan Li. Time-Series Analysis on Persistent Scatter-Interferometric Synthetic Aperture Radar (PS-InSAR) Derived Displacements of the Hong Kong–Zhuhai–Macao Bridge (HZMB) from Sentinel-1A Observations. Remote Sensing. 2021; 13 (4):546.

Chicago/Turabian Style

Siting Xiong; Chisheng Wang; Xiaoqiong Qin; Bochen Zhang; Qingquan Li. 2021. "Time-Series Analysis on Persistent Scatter-Interferometric Synthetic Aperture Radar (PS-InSAR) Derived Displacements of the Hong Kong–Zhuhai–Macao Bridge (HZMB) from Sentinel-1A Observations." Remote Sensing 13, no. 4: 546.

Journal article
Published: 20 January 2021 in IEEE Transactions on Geoscience and Remote Sensing
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Hyperspectral images encompass abundant information and provide unique characteristics for material classification. However, the labeling of training samples can be challenging in hyperspectral image classification. To address this problem, this study proposes a framework named flexible Gabor-based superpixel-level unsupervised linear discriminant analysis (FG-SuULDA) to extract the most informative and discriminating features for classification. First, a number of 3-D flexible Gabor filters are rigorously designed using an asymmetric sinusoidal wave to sufficiently characterize the spatial-spectral structure in hyperspectral images. Then, an unsupervised linear discriminant analysis strategy guided by the entropy rate superpixel (ERS) segmentation algorithm, called SuULDA, is skillfully introduced to reduce the extracted large amount of FG features. The SuULDA method not only boosts the classification capability but also increases the peculiarity of features, with the aid of superpixel information. Finally, the achieved features are imported to the popular support vector machine classifier. The proposed FG-SuULDA framework is applied to four real hyperspectral image data sets, and the experiments constantly prove that our FG-SuULDA is superior to several state-of-the-art methods in both classification performance and computational efficiency, especially with scarce training samples. The codes of this work are available at http://jiasen.tech/papers/ for the sake of reproducibility.

ACS Style

Sen Jia; Qingqing Zhao; Jiayue Zhuang; Dingding Tang; Yaqian Long; Meng Xu; Jun Zhou; Qingquan Li. Flexible Gabor-Based Superpixel-Level Unsupervised LDA for Hyperspectral Image Classification. IEEE Transactions on Geoscience and Remote Sensing 2021, PP, 1 -16.

AMA Style

Sen Jia, Qingqing Zhao, Jiayue Zhuang, Dingding Tang, Yaqian Long, Meng Xu, Jun Zhou, Qingquan Li. Flexible Gabor-Based Superpixel-Level Unsupervised LDA for Hyperspectral Image Classification. IEEE Transactions on Geoscience and Remote Sensing. 2021; PP (99):1-16.

Chicago/Turabian Style

Sen Jia; Qingqing Zhao; Jiayue Zhuang; Dingding Tang; Yaqian Long; Meng Xu; Jun Zhou; Qingquan Li. 2021. "Flexible Gabor-Based Superpixel-Level Unsupervised LDA for Hyperspectral Image Classification." IEEE Transactions on Geoscience and Remote Sensing PP, no. 99: 1-16.

Journal article
Published: 20 January 2021 in IEEE Transactions on Geoscience and Remote Sensing
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Due to the importance in many military and civilian applications, hyperspectral anomaly detection has attracted remarkable interest. Low-rank representation (LRR)-based anomaly detectors use the low-rank property to represent background pixels, and pixels that cannot be well represented are detected as anomalies. The ability of an LRR-based detector to separate background pixels and anomalous pixels depends on the dictionary representation ability, which usually can be enhanced by designing a proper prior for dictionary representation coefficients and constructing a better dictionary. However, it is not easy to handcraft effective and meaningful regularizers for dictionary coefficients. In this article, we propose a novel anomaly detection algorithm that uses a plug-and-play prior for representation coefficients and constructs a new dictionary based on clustering. Instead of cumbersomely handcrafting a regularizer for representation coefficients, we propose solving the anomaly detection problem using the plug-and-play framework, which enables us to plug state-of-the-art priors for representation coefficients. An effective convolutional neural network (CNN) denoiser is plugged into our framework to fully exploit the spatial correlation of representation coefficients. We also propose a modified background dictionary construction method, which carefully includes background pixels and excludes anomalous pixels from clustering results. We refer to the proposed anomaly detection method as plug-and-play denoising CNN regularized anomaly detection (DeCNN-AD) method. Extensive experiments were performed on five data sets in a comparison with eight state-of-the-art anomaly detection methods. The experimental results suggest that the proposed method is effective in anomaly detection and can produce better anomaly detection results than that of the comparison methods. The codes of this work will be available at https://github.com/FxyPd for the sake of reproducibility.

ACS Style

Xiyou Fu; Sen Jia; Lina Zhuang; Meng Xu; Jun Zhou; Qingquan Li. Hyperspectral Anomaly Detection via Deep Plug-and-Play Denoising CNN Regularization. IEEE Transactions on Geoscience and Remote Sensing 2021, PP, 1 -16.

AMA Style

Xiyou Fu, Sen Jia, Lina Zhuang, Meng Xu, Jun Zhou, Qingquan Li. Hyperspectral Anomaly Detection via Deep Plug-and-Play Denoising CNN Regularization. IEEE Transactions on Geoscience and Remote Sensing. 2021; PP (99):1-16.

Chicago/Turabian Style

Xiyou Fu; Sen Jia; Lina Zhuang; Meng Xu; Jun Zhou; Qingquan Li. 2021. "Hyperspectral Anomaly Detection via Deep Plug-and-Play Denoising CNN Regularization." IEEE Transactions on Geoscience and Remote Sensing PP, no. 99: 1-16.

Journal article
Published: 20 January 2021 in IEEE Transactions on Intelligent Transportation Systems
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Short-term traffic prediction is of great importance to the management of traffic congestion, a pervasive and difficult-to-solve problem in many metropolises all over the world. However, existing studies on traffic prediction contain rough traffic information at the carriageway level that ignore the distinction between different turns in one intersection. With the aim of predicting traffic at road intersections from big trace data on a finer scale, this study proposes a novel method, the fine-grained traffic prediction method (FTPG) with a graph attention network (GAT), which predicts traffic information, including traffic flow speeds, traffic states, and average queue lengths, at the turn level. In the FTPG, a method for estimation of the queue starting point is proposed to improve the accuracy of traffic information detection. Furthermore, the topology is constructed under turn-level conditions, and a GAT-based method, the spatio-temporal residual graph attention network (ST-RGAN), is proposed to improve the prediction accuracy. Experiments are performed using taxi GPS trace data collected in the city of Wuhan and show that the proposed FTPG method can make predictions with fine-grained traffic information for road intersections accurately and robustly.

ACS Style

Mengyuan Fang; Luliang Tang; Xue Yang; Yang Chen; Chaokui Li; Qingquan Li. FTPG: A Fine-Grained Traffic Prediction Method With Graph Attention Network Using Big Trace Data. IEEE Transactions on Intelligent Transportation Systems 2021, PP, 1 -13.

AMA Style

Mengyuan Fang, Luliang Tang, Xue Yang, Yang Chen, Chaokui Li, Qingquan Li. FTPG: A Fine-Grained Traffic Prediction Method With Graph Attention Network Using Big Trace Data. IEEE Transactions on Intelligent Transportation Systems. 2021; PP (99):1-13.

Chicago/Turabian Style

Mengyuan Fang; Luliang Tang; Xue Yang; Yang Chen; Chaokui Li; Qingquan Li. 2021. "FTPG: A Fine-Grained Traffic Prediction Method With Graph Attention Network Using Big Trace Data." IEEE Transactions on Intelligent Transportation Systems PP, no. 99: 1-13.

Article
Published: 13 January 2021 in Chinese Geographical Science
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China has experienced rapid urbanizations with dramatic land cover changes since 1978. Forest loss is one of land cover changes, and it induces various eco-environmental degradation issues. As one of China’s hotspot regions, the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) has undergone a dramatic urban expansion. To better understand forest dynamics and protect forest ecosystem, revealing the processes, patterns and underlying drivers of forest loss is essential. This study focused on the spatiotemporal evolution and potential driving factors of forest loss in the GBA at regional and city level. The Landsat time-series images from 1987 to 2017 were used to derive forest, and landscape metrics and geographic information system (GIS) were applied to implement further spatial analysis. The results showed that: 1) 14.86% of the total urban growth area of the GBA was obtained from the forest loss in 1987–2017; meanwhile, the forest loss area of the GBA reached 4040.6 km2, of which 25.60% (1034.42 km2) was converted to urban land; 2) the percentages of forest loss to urban land in Dongguan (19.14%), Guangzhou (18.35%) and Shenzhen (15.81%) were higher than those in other cities; 3) the forest became increasingly fragmented from 1987–2007, and then the fragmentation decreased from 2007 to 2017); 4) the landscape responses to forest changes varied with the scale; and 5) some forest loss to urban regions moved from low-elevation and gentle-slope terrains to higher-elevation and steep-slope terrains over time, especially in Shenzhen and Hong Kong. Urbanization and industrialization greatly drove forest loss and fragmentation, and, notably, hillside urban land expansion may have contributed to hillside forest loss. The findings will help policy makers in maintaining the stability of forest ecosystems, and provide some new insights into forest management and conservation.

ACS Style

Chao Yang; Huizeng Liu; Qingquan Li; Aihong Cui; Rongling Xia; Tiezhu Shi; Jie Zhang; Wenxiu Gao; Xiang Zhou; Guofeng Wu. Rapid Urbanization Induced Extensive Forest Loss to Urban Land in the Guangdong-Hong Kong-Macao Greater Bay Area, China. Chinese Geographical Science 2021, 31, 93 -108.

AMA Style

Chao Yang, Huizeng Liu, Qingquan Li, Aihong Cui, Rongling Xia, Tiezhu Shi, Jie Zhang, Wenxiu Gao, Xiang Zhou, Guofeng Wu. Rapid Urbanization Induced Extensive Forest Loss to Urban Land in the Guangdong-Hong Kong-Macao Greater Bay Area, China. Chinese Geographical Science. 2021; 31 (1):93-108.

Chicago/Turabian Style

Chao Yang; Huizeng Liu; Qingquan Li; Aihong Cui; Rongling Xia; Tiezhu Shi; Jie Zhang; Wenxiu Gao; Xiang Zhou; Guofeng Wu. 2021. "Rapid Urbanization Induced Extensive Forest Loss to Urban Land in the Guangdong-Hong Kong-Macao Greater Bay Area, China." Chinese Geographical Science 31, no. 1: 93-108.

Journal article
Published: 11 January 2021 in IEEE Sensors Journal
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The Fine Time Measurement (FTM) protocol introduced by IEEE 802.11 includes a new ranging method, named Wi-Fi Round Trip Time (Wi-Fi RTT), which can be used for indoor localization. Pedestrian Dead Reckoning (PDR) can provide accurate pedestrian tracking through inertial sensors in a short time. Information fusion of PDR and existing wireless technology is widely used in indoor localization to ensure the robustness and stability. In this paper, we propose a fusion indoor localization method of Wi-Fi RTT and PDR. Firstly, an adaptive filtering system consisting of multiple Extended Kalman Filter (EKF) and a new outlier detection method is proposed to reduce the localization error of Wi-Fi RTT. Secondly, the fusion algorithm based on the Federated Filter (FF) and observability is designed to combine Wi-Fi RTT with PDR. Finally, to further improve the localization performance of the fusion algorithm, a real-time smoothing method with fixed interval is used. We evaluate the proposed method in four different scenarios. The results show that the proposed indoor localization method has better stability and robustness, and the average localization error decreased by 37.4-67.6% compared with the classic EKF-based method.

ACS Style

Xu Liu; Baoding Zhou; Panpan Huang; Weixing Xue; Qingquan Li; Jiasong Zhu; Li Qiu. Kalman Filter-Based Data Fusion of Wi-Fi RTT and PDR for Indoor Localization. IEEE Sensors Journal 2021, 21, 8479 -8490.

AMA Style

Xu Liu, Baoding Zhou, Panpan Huang, Weixing Xue, Qingquan Li, Jiasong Zhu, Li Qiu. Kalman Filter-Based Data Fusion of Wi-Fi RTT and PDR for Indoor Localization. IEEE Sensors Journal. 2021; 21 (6):8479-8490.

Chicago/Turabian Style

Xu Liu; Baoding Zhou; Panpan Huang; Weixing Xue; Qingquan Li; Jiasong Zhu; Li Qiu. 2021. "Kalman Filter-Based Data Fusion of Wi-Fi RTT and PDR for Indoor Localization." IEEE Sensors Journal 21, no. 6: 8479-8490.

Journal article
Published: 24 December 2020 in IEEE Transactions on Geoscience and Remote Sensing
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Landsat images have played an important role in the field of Earth observation and geoinformatics. However, optical Landsat images are frequently contaminated by cloud cover, especially in tropical and subtropical regions, which limits the utilization of these images. To improve the utilization of Landsat images, in this study, we propose a novel spatiotemporal neural network with four modules: a cloud detection module, a spatial-temporal learning module, a spatial-temporal feature fusion module, and a reconstruction module. The results of the experiments demonstrate that the proposed method is quantitatively effective (root mean square error < 0.0179) and can achieve a better result for reconstructing Landsat images than some of the widely used existing deep learning methods and multitemporal methods. The proposed neural network method provides an effective tool for the removal of contiguous, thick clouds from satellite images, so as to improve the quality of subsequent remote sensing mapping and geoinformation extraction.

ACS Style

Yang Chen; Qihao Weng; Luliang Tang; Xia Zhang; Muhammad Bilal; Qingquan Li. Thick Clouds Removing From Multitemporal Landsat Images Using Spatiotemporal Neural Networks. IEEE Transactions on Geoscience and Remote Sensing 2020, PP, 1 -14.

AMA Style

Yang Chen, Qihao Weng, Luliang Tang, Xia Zhang, Muhammad Bilal, Qingquan Li. Thick Clouds Removing From Multitemporal Landsat Images Using Spatiotemporal Neural Networks. IEEE Transactions on Geoscience and Remote Sensing. 2020; PP (99):1-14.

Chicago/Turabian Style

Yang Chen; Qihao Weng; Luliang Tang; Xia Zhang; Muhammad Bilal; Qingquan Li. 2020. "Thick Clouds Removing From Multitemporal Landsat Images Using Spatiotemporal Neural Networks." IEEE Transactions on Geoscience and Remote Sensing PP, no. 99: 1-14.

Journal article
Published: 17 November 2020 in IEEE Internet of Things Journal
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With the promotion of low-carbon travel, pedestrian network plays an important role in many location-based applications, such as pedestrian navigation and refined traffic management. Due to the lack of systematic data acquisition mechanics, the accuracies and detail levels of pedestrian network data are hardly capable of satisfying the demands of such transportation applications. Presently, various mobile phone apps recorded and stored users’ movement trajectories, which provide a valuable data source for pedestrian network construction. Hence, this paper proposes a crowdsourcing-based system for generating pedestrian network that encompasses three key components of crowdsourced walking trajectory data filtering, pedestrian network construction and evaluation of pedestrian network. Self-collected data and open platform data were used to evaluate the proposed system. Experimental results demonstrate that the proposed method can accurately and completely extract pedestrian network. Moreover, the pedestrian network can be updated in a timely manner by the proposed method. The data collection application and the collected data are available to the public.

ACS Style

Baoding Zhou; Tianjing Zheng; Jincai Huang; Yunfei Zhang; Wei Tu; Qingquan Li; Min Deng. A Pedestrian Network Construction System Based on Crowdsourced Walking Trajectories. IEEE Internet of Things Journal 2020, 8, 7203 -7213.

AMA Style

Baoding Zhou, Tianjing Zheng, Jincai Huang, Yunfei Zhang, Wei Tu, Qingquan Li, Min Deng. A Pedestrian Network Construction System Based on Crowdsourced Walking Trajectories. IEEE Internet of Things Journal. 2020; 8 (9):7203-7213.

Chicago/Turabian Style

Baoding Zhou; Tianjing Zheng; Jincai Huang; Yunfei Zhang; Wei Tu; Qingquan Li; Min Deng. 2020. "A Pedestrian Network Construction System Based on Crowdsourced Walking Trajectories." IEEE Internet of Things Journal 8, no. 9: 7203-7213.

Journal article
Published: 04 September 2020 in IEEE Transactions on Vehicular Technology
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A new method is proposed for selecting Access Points (APs) with propagation direction combination based on Eight-Diagram for indoor localization in indoor environments, which can not only improve positioning accuracy, but also reduce computational complexity by using fewer APs. With propagation direction combination based on eight basic directions, for instance, east, south, west, north, southeast, northeast, southwest and northwest, provided by the Eight-Diagram, the proposed AP selection algorithm can be applied to indoor scenarios where Wi-Fi RSSI is corrupted by multipath interference. Experiments were conducted and the results demonstrate that the proposed AP selection algorithm achieves an accuracy considerably better than the WKNN, MaxMean, InfoGain and PCA methods. Due to the use of fewer APs, the proposed algorithm has lower computational complexity than the MaxMean and InfoGain algorithms, and equivalent with the PCA algorithm.

ACS Style

Weixing Xue; Kegen Yu; Qingquan Li; Baoding Zhou; Jiasong Zhu; Yuwei Chen; Weining Qiu; Xianghong Hua; Wei Ma; Zhipeng Chen. Eight-Diagram Based Access Point Selection Algorithm for Indoor Localization. IEEE Transactions on Vehicular Technology 2020, 69, 13196 -13205.

AMA Style

Weixing Xue, Kegen Yu, Qingquan Li, Baoding Zhou, Jiasong Zhu, Yuwei Chen, Weining Qiu, Xianghong Hua, Wei Ma, Zhipeng Chen. Eight-Diagram Based Access Point Selection Algorithm for Indoor Localization. IEEE Transactions on Vehicular Technology. 2020; 69 (11):13196-13205.

Chicago/Turabian Style

Weixing Xue; Kegen Yu; Qingquan Li; Baoding Zhou; Jiasong Zhu; Yuwei Chen; Weining Qiu; Xianghong Hua; Wei Ma; Zhipeng Chen. 2020. "Eight-Diagram Based Access Point Selection Algorithm for Indoor Localization." IEEE Transactions on Vehicular Technology 69, no. 11: 13196-13205.