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Human settlements are guided by the proximity or availability of a natural resource such as river or lake basins containing set of streams. The harmonious development of human activity and natural conditions along watershed areas needs close attention and in-depth study. In this paper, the urban agglomerations and ecological spaces in the Yangtze River Delta, China, the Chao Lake Basin and its surrounding watershed ecosystem is taken as research subject for its serious environmental degradation problems during social and economic development. This paper adopted an effective machine learning algorithm (kernel-ELM) to extract land use and land /cover information, and to analyze the land use/cover pattern evolution rules of the Chao Lake Basin with long term Landsat imagery. Subsequent studies were then carried out to demonstrate the flood-affected area and its ecological impact in the basin in 2020, to reveal the occupation on land cover types. The results indicate Conclusions are drawn from the experiment results: (1) There has been significant change in cultivated land, forest land and construction land out of six key land cover types with dynamic degree of −10.17%, 4.61, 67.04% respectively. (2) Algae bloom pollution was extracted from pattern classification results and it was up to 15% of the total water area by the year 2018. (3) The occupation on land use/cover types of the flood was revealed. The results prove effective application of remote sensing technology in environmental analysis and planning for data-driven evaluation of governing policy. This work serves as a scientific basis for environmental management and regional planning in the Chao Lake Basin and can be served as a basis and a reference for evaluating an ecological policy and its impact for other economic developing watershed human settlements with ecological issues.
Yi Lin; Tinghui Zhang; Qin Ye; Jianqing Cai; Chengzhao Wu; Awase Khirni Syed; Jonathan Li. Long-term remote sensing monitoring on LUCC around Chaohu Lake with new information of algal bloom and flood submerging. International Journal of Applied Earth Observation and Geoinformation 2021, 102, 102413 .
AMA StyleYi Lin, Tinghui Zhang, Qin Ye, Jianqing Cai, Chengzhao Wu, Awase Khirni Syed, Jonathan Li. Long-term remote sensing monitoring on LUCC around Chaohu Lake with new information of algal bloom and flood submerging. International Journal of Applied Earth Observation and Geoinformation. 2021; 102 ():102413.
Chicago/Turabian StyleYi Lin; Tinghui Zhang; Qin Ye; Jianqing Cai; Chengzhao Wu; Awase Khirni Syed; Jonathan Li. 2021. "Long-term remote sensing monitoring on LUCC around Chaohu Lake with new information of algal bloom and flood submerging." International Journal of Applied Earth Observation and Geoinformation 102, no. : 102413.
Herein, we propose a novel indoor structure extraction (ISE) method that can reconstruct an indoor planar structure with a feature structure map (FSM) and enable indoor robot navigation using a navigation structure map (NSM). To construct the FSM, we first propose a two-staged region growing algorithm to segment the planar feature and to obtain the original planar point cloud. Subsequently, we simplify the planar feature using quadtree segmentation based on cluster fusion. Finally, we perform simple triangulation in the interior and vertex-assignment triangulation in the boundary to accomplish feature reconstruction for the planar structure. The FSM is organized in the form of a mesh model. To construct the NSM, we first propose a novel ground extraction method based on indoor structure analysis under the Manhattan world assumption. It can accurately capture the ground plane in an indoor scene. Subsequently, we establish a passable area map (PAM) within different heights. Finally, a novel-form NSM is established using the original planar point cloud and the PAM. Experiments are performed using three public datasets and one self-collected dataset. The proposed plane segmentation approach is evaluated on two simulation datasets and achieves a recall of approximately 99%, which is 5% higher than that of the traditional plane segmentation method. Furthermore, the triangulation performance of our method compared with the traditional greedy projection triangulation show that our method performs better in terms of feature representation. The experimental results reveal that our ISE method is robust and effective for extracting indoor structures.
Pengcheng Shi; Qin Ye; Lingwen Zeng. A Novel Indoor Structure Extraction Based on Dense Point Cloud. ISPRS International Journal of Geo-Information 2020, 9, 660 .
AMA StylePengcheng Shi, Qin Ye, Lingwen Zeng. A Novel Indoor Structure Extraction Based on Dense Point Cloud. ISPRS International Journal of Geo-Information. 2020; 9 (11):660.
Chicago/Turabian StylePengcheng Shi; Qin Ye; Lingwen Zeng. 2020. "A Novel Indoor Structure Extraction Based on Dense Point Cloud." ISPRS International Journal of Geo-Information 9, no. 11: 660.
Reducing the cumulative error is a crucial task in simultaneous localization and mapping (SLAM). Usually, Loop Closure Detection (LCD) is exploited to accomplish this work for SLAM and robot navigation. With a fast and accurate loop detection, it can significantly improve global localization stability and reduce mapping errors. However, the LCD task based on point cloud still has some problems, such as over-reliance on high-resolution sensors, and poor detection efficiency and accuracy. Therefore, in this paper, we propose a novel and fast global LCD method using a low-cost 16 beam Lidar based on “Simplified Structure”. Firstly, we extract the “Simplified Structure” from the indoor point cloud, classify them into two levels, and manage the “Simplified Structure” hierarchically according to its structure salience. The “Simplified Structure” has simple feature geometry and can be exploited to capture the indoor stable structures. Secondly, we analyze the point cloud registration suitability with a pre-match, and present a hierarchical matching strategy with multiple geometric constraints in Euclidean Space to match two scans. Finally, we construct a multi-state loop evaluation model for a multi-level structure to determine whether the two candidate scans are a loop. In fact, our method also provides a transformation for point cloud registration with “Simplified Structure” when a loop is detected successfully. Experiments are carried out on three types of indoor environment. A 16 beam Lidar is used to collect data. The experimental results demonstrate that our method can detect global loop closures efficiently and accurately. The average global LCD precision, accuracy and negative are approximately 0.90, 0.96, and 0.97, respectively.
Qin Ye; Pengcheng Shi; Kunyuan Xu; PoPo Gui; Shaoming Zhang. A Novel Loop Closure Detection Approach Using Simplified Structure for Low-Cost LiDAR. Sensors 2020, 20, 2299 .
AMA StyleQin Ye, Pengcheng Shi, Kunyuan Xu, PoPo Gui, Shaoming Zhang. A Novel Loop Closure Detection Approach Using Simplified Structure for Low-Cost LiDAR. Sensors. 2020; 20 (8):2299.
Chicago/Turabian StyleQin Ye; Pengcheng Shi; Kunyuan Xu; PoPo Gui; Shaoming Zhang. 2020. "A Novel Loop Closure Detection Approach Using Simplified Structure for Low-Cost LiDAR." Sensors 20, no. 8: 2299.
Real-time and high-precision localization information is vital for many modules of unmanned vehicles. At present, a high-cost RTK (Real Time Kinematic) and IMU (Integrated Measurement Unit) integrated navigation system is often used, but its accuracy cannot meet the requirements and even fails in many scenes. In order to reduce the costs and improve the localization accuracy and stability, we propose a precise and robust segmentation-based Lidar (Light Detection and Ranging) localization system aided with MEMS (Micro-Electro-Mechanical System) IMU and designed for high level autonomous driving. Firstly, we extracted features from the online frame using a series of proposed efficient low-level semantic segmentation-based multiple types feature extraction algorithms, including ground, road-curb, edge, and surface. Next, we matched the adjacent frames in Lidar odometry module and matched the current frame with the dynamically loaded pre-build feature point cloud map in Lidar localization module based on the extracted features to precisely estimate the 6DoF (Degree of Freedom) pose, through the proposed priori information considered category matching algorithm and multi-group-step L-M (Levenberg-Marquardt) optimization algorithm. Finally, the lidar localization results were fused with MEMS IMU data through a state-error Kalman filter to produce smoother and more accurate localization information at a high frequency of 200Hz. The proposed localization system can achieve 3~5 cm in position and 0.05~0.1° in orientation RMS (Root Mean Square) accuracy and outperform previous state-of-the-art systems. The robustness and adaptability have been verified with localization testing data more than 1000 Km in various challenging scenes, including congested urban roads, narrow tunnels, textureless highways, and rain-like harsh weather.
Hang Liu; Qin Ye; Hairui Wang; Liang Chen; Jian Yang. A Precise and Robust Segmentation-Based Lidar Localization System for Automated Urban Driving. Remote Sensing 2019, 11, 1348 .
AMA StyleHang Liu, Qin Ye, Hairui Wang, Liang Chen, Jian Yang. A Precise and Robust Segmentation-Based Lidar Localization System for Automated Urban Driving. Remote Sensing. 2019; 11 (11):1348.
Chicago/Turabian StyleHang Liu; Qin Ye; Hairui Wang; Liang Chen; Jian Yang. 2019. "A Precise and Robust Segmentation-Based Lidar Localization System for Automated Urban Driving." Remote Sensing 11, no. 11: 1348.
The registration of 3-D point clouds is an important procedure during the terrestrial laser scanning data processing. Recently, due to their high flexibility and the powerful mathematical model, a large amount of least-squares-based (LSs-based) methods are proposed and widely applied to estimate the transformation parameters of 3-D point clouds registration. In these LSs-based methods some based on the generalized Gauss-Markov model do not correct the influence of random errors on source 3-D point clouds. Although there are other methods based on the errors-in-variables (EIV) model, they are inapplicable for transformation problems with large rotation angles and arbitrary scale ratio. In addition, the gross errors are usually ignored in previous studies on 3-D point clouds registration, which, however, exists commonly and could distort the registration severely. Aiming to avoid the influence of gross errors and extend its application, an advanced outlier detected total least-squares (OD-TLS) method is proposed in this paper. Based on the generalized EIV model OD-TLS performs a seven-parameter 3-D similarity transformation with large rotation angles and arbitrary scale ratio. The random errors of both source and target 3-D point clouds are considered. Furthermore, outliers are detected and removed automatically by combining the data snooping method with total least-squares (TLS) estimation. In order to indicate the benefits of OD-TLS, comparative experiments with the LS3D and weighted total least squares (WTLS) on synthetic and real-world scanned 3-D point clouds were performed. The experimental results show OD-TLS not only enhances the registration accuracy but also increases its robustness.
Jie Yu; Yi Lin; Bin Wang; Qin Ye; Jianqing Cai. An Advanced Outlier Detected Total Least-Squares Algorithm for 3-D Point Clouds Registration. IEEE Transactions on Geoscience and Remote Sensing 2019, 57, 4789 -4798.
AMA StyleJie Yu, Yi Lin, Bin Wang, Qin Ye, Jianqing Cai. An Advanced Outlier Detected Total Least-Squares Algorithm for 3-D Point Clouds Registration. IEEE Transactions on Geoscience and Remote Sensing. 2019; 57 (7):4789-4798.
Chicago/Turabian StyleJie Yu; Yi Lin; Bin Wang; Qin Ye; Jianqing Cai. 2019. "An Advanced Outlier Detected Total Least-Squares Algorithm for 3-D Point Clouds Registration." IEEE Transactions on Geoscience and Remote Sensing 57, no. 7: 4789-4798.