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Mrs. Run Wang
China University of Geosciences (Wuhan)

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Research Keywords & Expertise

0 Remote Sensing
0 Route Planning
0 Spatial Analysis
0 Remote sensing & GIS applications
0 3D geological modeling

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Journal article
Published: 23 November 2020 in Sensors
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Imbalanced learning is a common problem in remote sensing imagery-based land-use and land-cover classifications. Imbalanced learning can lead to a reduction in classification accuracy and even the omission of the minority class. In this paper, an impartial semi-supervised learning strategy based on extreme gradient boosting (ISS-XGB) is proposed to classify very high resolution (VHR) images with imbalanced data. ISS-XGB solves multi-class classification by using several semi-supervised classifiers. It first employs multi-group unlabeled data to eliminate the imbalance of training samples and then utilizes gradient boosting-based regression to simulate the target classes with positive and unlabeled samples. In this study, experiments were conducted on eight study areas with different imbalanced situations. The results showed that ISS-XGB provided a comparable but more stable performance than most commonly used classification approaches (i.e., random forest (RF), XGB, multilayer perceptron (MLP), and support vector machine (SVM)), positive and unlabeled learning (PU-Learning) methods (PU-BP and PU-SVM), and typical synthetic sample-based imbalanced learning methods. Especially under extremely imbalanced situations, ISS-XGB can provide high accuracy for the minority class without losing overall performance (the average overall accuracy achieves 85.92%). The proposed strategy has great potential in solving the imbalanced classification problems in remote sensing.

ACS Style

Fei Sun; Fang Fang; Run Wang; Bo Wan; Qinghua Guo; Hong Li; Xincai Wu. An Impartial Semi-supervised Learning Strategy for Imbalanced Classification on VHR Images. Sensors 2020, 20, 6699 .

AMA Style

Fei Sun, Fang Fang, Run Wang, Bo Wan, Qinghua Guo, Hong Li, Xincai Wu. An Impartial Semi-supervised Learning Strategy for Imbalanced Classification on VHR Images. Sensors. 2020; 20 (22):6699.

Chicago/Turabian Style

Fei Sun; Fang Fang; Run Wang; Bo Wan; Qinghua Guo; Hong Li; Xincai Wu. 2020. "An Impartial Semi-supervised Learning Strategy for Imbalanced Classification on VHR Images." Sensors 20, no. 22: 6699.

Journal article
Published: 16 September 2019 in Remote Sensing
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Hainan Island is the second-largest island in China and has the most species-diverse mangrove forests in the country. To date, the height and aboveground ground biomass (AGB) of the mangrove forests on Hainan Island are unknown, partly as a result of the challenges faced during extensive field sampling in mangrove habitats (intertidal mudflats inundated by periodic seawater). Therefore, this study used a low-cost UAV-LiDAR (light detection and ranging sensor mounted on an unmanned aerial vehicle) system as a sampling tool and Sentinel-2 imagery as auxiliary data to estimate and map the mangrove height and AGB on Hainan Island. Hainan Island has 3697.02 hectares of mangrove forests with an average patch area of approximately 1 ha. The results show that the mangroves on whole Hainan Island have an average height of 6.99 m, a total AGB of 474,199.31 Mg and an AGB density of 128.27 Mg ha−1. The AGB hot spots are located in Qinglan Harbor and the south of Dongzhai Harbor. The proposed height model LiDAR-S2 performed well with an R2 of 0.67 and an RMSE (root mean square error) of 1.90 m; the proposed AGB model G~LiDAR~S2 performed better (an R2 of 0.62 and an RMSE of 50.36 Mg ha−1) than the traditional AGB model G~S2 that directly related ground plots and Sentinel-2 data. The results also indicate that the LiDAR metrics describing the canopy’s thickness and its top and bottom characteristics are the most important variables for mangrove AGB estimation. For the Sentinel-2 indices, the red-edge and shortwave infrared features, especially the red-edge 1 and shortwave infrared Band 11 features, play the most important roles in estimating mangrove AGB and height. In conclusion, this paper presents the first mangrove height and AGB maps of Hainan Island and demonstrates the feasibility of using UAV-LiDAR as a sampling tool for mangrove forests.

ACS Style

Dezhi Wang; Bo Wan; Penghua Qiu; Zejun Zuo; Run Wang; Xincai Wu. Mapping Height and Aboveground Biomass of Mangrove Forests on Hainan Island Using UAV-LiDAR Sampling. Remote Sensing 2019, 11, 2156 .

AMA Style

Dezhi Wang, Bo Wan, Penghua Qiu, Zejun Zuo, Run Wang, Xincai Wu. Mapping Height and Aboveground Biomass of Mangrove Forests on Hainan Island Using UAV-LiDAR Sampling. Remote Sensing. 2019; 11 (18):2156.

Chicago/Turabian Style

Dezhi Wang; Bo Wan; Penghua Qiu; Zejun Zuo; Run Wang; Xincai Wu. 2019. "Mapping Height and Aboveground Biomass of Mangrove Forests on Hainan Island Using UAV-LiDAR Sampling." Remote Sensing 11, no. 18: 2156.

Journal article
Published: 23 July 2019 in ISPRS International Journal of Geo-Information
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Imbalanced learning is a methodological challenge in remote sensing communities, especially in complex areas where the spectral similarity exists between land covers. Obtaining high-confidence classification results for imbalanced class issues is highly important in practice. In this paper, extreme gradient boosting (XGB), a novel tree-based ensemble system, is employed to classify the land cover types in Very-high resolution (VHR) images with imbalanced training data. We introduce an extended margin criterion and disagreement performance to evaluate the efficiency of XGB in imbalanced learning situations and examine the effect of minority class spectral separability on model performance. The results suggest that the uncertainty of XGB associated with correct classification is stable. The average probability-based margin of correct classification provided by XGB is 0.82, which is about 46.30% higher than that by random forest (RF) method (0.56). Moreover, the performance uncertainty of XGB is insensitive to spectral separability after the sample imbalance reached a certain level (minority:majority > 10:100). The impact of sample imbalance on the minority class is also related to its spectral separability, and XGB performs better than RF in terms of user accuracy for the minority class with imperfect separability. The disagreement components of XGB are better and more stable than RF with imbalanced samples, especially for complex areas with more types. In addition, appropriate sample imbalance helps to improve the trade-off between the recognition accuracy of XGB and the sample cost. According to our analysis, this margin-based uncertainty assessment and disagreement performance can help users identify the confidence level and error component in similar classification performance (overall, producer, and user accuracies).

ACS Style

Fei Sun; Run Wang; Bo Wan; Yanjun Su; Qinghua Guo; Youxin Huang; Xincai Wu. Efficiency of Extreme Gradient Boosting for Imbalanced Land Cover Classification Using an Extended Margin and Disagreement Performance. ISPRS International Journal of Geo-Information 2019, 8, 315 .

AMA Style

Fei Sun, Run Wang, Bo Wan, Yanjun Su, Qinghua Guo, Youxin Huang, Xincai Wu. Efficiency of Extreme Gradient Boosting for Imbalanced Land Cover Classification Using an Extended Margin and Disagreement Performance. ISPRS International Journal of Geo-Information. 2019; 8 (7):315.

Chicago/Turabian Style

Fei Sun; Run Wang; Bo Wan; Yanjun Su; Qinghua Guo; Youxin Huang; Xincai Wu. 2019. "Efficiency of Extreme Gradient Boosting for Imbalanced Land Cover Classification Using an Extended Margin and Disagreement Performance." ISPRS International Journal of Geo-Information 8, no. 7: 315.

Research articles
Published: 18 April 2019 in International Journal of Geographical Information Science
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During navigation, a pedestrian needs to recognize a landmark at a certain decision point. If a potential landmark located at a decision point is complicated to recognize, the complexity of the decision point is significantly increased. Thus, it is important to compute routes that avoid complicated decision points (CDPs) but still achieve optimal navigation performance. In this paper, we propose an approach for computing routes that avoid CDPs while optimizing the performance of landmark-based pedestrian navigation. The approach includes (1) a model for identifying CDPs based on the structures of pedestrian networks and landmark data in real scenes, and (2) a modified genetic algorithm for computing routes that avoid the identified CDPs and find the shortest route possible. To demonstrate the advantages and effectiveness of the proposed approach, we conducted an empirical study on the pedestrian network in a real-world scenario. The experimental results show that our approach can effectively avoid CDPs while still minimizing travel distance. Furthermore, our approach can provide the routes with the shortest travel distance if the distances of the routes without CDPs exceed a certain threshold.

ACS Style

Sha Zhou; Run Wang; Junhua Ding; Xiaofang Pan; Shunping Zhou; Fang Fang; Wenjie Zhen. An approach for computing routes without complicated decision points in landmark-based pedestrian navigation. International Journal of Geographical Information Science 2019, 33, 1829 -1846.

AMA Style

Sha Zhou, Run Wang, Junhua Ding, Xiaofang Pan, Shunping Zhou, Fang Fang, Wenjie Zhen. An approach for computing routes without complicated decision points in landmark-based pedestrian navigation. International Journal of Geographical Information Science. 2019; 33 (9):1829-1846.

Chicago/Turabian Style

Sha Zhou; Run Wang; Junhua Ding; Xiaofang Pan; Shunping Zhou; Fang Fang; Wenjie Zhen. 2019. "An approach for computing routes without complicated decision points in landmark-based pedestrian navigation." International Journal of Geographical Information Science 33, no. 9: 1829-1846.

Journal article
Published: 07 December 2018 in ISPRS International Journal of Geo-Information
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The road-network matching method is an effective tool for map integration, fusion, and update. Due to the complexity of road networks in the real world, matching methods often contain a series of complicated processes to identify homonymous roads and deal with their intricate relationship. However, traditional road-network matching algorithms, which are mainly central processing unit (CPU)-based approaches, may have performance bottleneck problems when facing big data. We developed a particle-swarm optimization (PSO)-based parallel road-network matching method on graphics-processing unit (GPU). Based on the characteristics of the two main stages (similarity computation and matching-relationship identification), data-partition and task-partition strategies were utilized, respectively, to fully use GPU threads. Experiments were conducted on datasets with 14 different scales. Results indicate that the parallel PSO-based matching algorithm (PSOM) could correctly identify most matching relationships with an average accuracy of 84.44%, which was at the same level as the accuracy of a benchmark—the probability-relaxation-matching (PRM) method. The PSOM approach significantly reduced the road-network matching time in dealing with large amounts of data in comparison with the PRM method. This paper provides a common parallel algorithm framework for road-network matching algorithms and contributes to integration and update of large-scale road-networks.

ACS Style

Bo Wan; Lin Yang; Shunping Zhou; Run Wang; Dezhi Wang; Wenjie Zhen. A Parallel-Computing Approach for Vector Road-Network Matching Using GPU Architecture. ISPRS International Journal of Geo-Information 2018, 7, 472 .

AMA Style

Bo Wan, Lin Yang, Shunping Zhou, Run Wang, Dezhi Wang, Wenjie Zhen. A Parallel-Computing Approach for Vector Road-Network Matching Using GPU Architecture. ISPRS International Journal of Geo-Information. 2018; 7 (12):472.

Chicago/Turabian Style

Bo Wan; Lin Yang; Shunping Zhou; Run Wang; Dezhi Wang; Wenjie Zhen. 2018. "A Parallel-Computing Approach for Vector Road-Network Matching Using GPU Architecture." ISPRS International Journal of Geo-Information 7, no. 12: 472.

Journal article
Published: 14 September 2018 in Remote Sensing
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Mapping mangrove extent and species is important for understanding their response to environmental changes and for observing their integrity for providing goods and services. However, accurately mapping mangrove extent and species are ongoing challenges in remote sensing. The newly-launched and freely-available Sentinel-2 (S2) sensor offers a new opportunity for these challenges. This study presents the first study dedicated to the examination of the potential of original bands, spectral indices, and texture information of S2 in mapping mangrove extent and species in the first National Nature Reserve for mangroves in Dongzhaigang, China. To map mangrove extent and species, a three-level hierarchical structure based on the spatial structure of a mangrove ecosystem and geographic object-based image analysis is utilized and modified. During the experiments, to conquer the challenge of optimizing high-dimension and correlated feature space, the recursive feature elimination (RFE) algorithm is introduced. Finally, the selected features from RFE are employed in mangrove species discriminations, based on a random forest algorithm. The results are compared with those of Landsat 8 (L8) and Pléiades-1 (P1) data and show that S2 and L8 could accurately extract mangrove extent, but P1 obviously overestimated it. Regarding mangrove species community levels, the overall classification accuracy of S2 is 70.95%, which is lower than P1 imagery (78.57%) and slightly higher than L8 data (68.57%). Meanwhile, the former difference is statistically significant, and the latter is not. The dominant species is extracted basically in S2 and P1 imagery, but for the occasionally distributed K. candel and the pioneer and fringe mangrove A. marina, S2 performs poorly. Concerning L8, S2, and P1, there are eight (8/126), nine (9/218), and eight (8/73) features, respectively, that are the most important for mangrove species discriminations. The most important feature overall is the red-edge bands, followed by shortwave infrared, near infrared, blue, and other visible bands in turn. This study demonstrates that the S2 sensor can accurately map mangrove extent and basically discriminate mangrove species communities, but for the latter, one should be cautious due to the complexity of mangrove species.

ACS Style

Dezhi Wang; Bo Wan; Penghua Qiu; Yanjun Su; Qinghua Guo; Run Wang; Fei Sun; Xincai Wu. Evaluating the Performance of Sentinel-2, Landsat 8 and Pléiades-1 in Mapping Mangrove Extent and Species. Remote Sensing 2018, 10, 1468 .

AMA Style

Dezhi Wang, Bo Wan, Penghua Qiu, Yanjun Su, Qinghua Guo, Run Wang, Fei Sun, Xincai Wu. Evaluating the Performance of Sentinel-2, Landsat 8 and Pléiades-1 in Mapping Mangrove Extent and Species. Remote Sensing. 2018; 10 (9):1468.

Chicago/Turabian Style

Dezhi Wang; Bo Wan; Penghua Qiu; Yanjun Su; Qinghua Guo; Run Wang; Fei Sun; Xincai Wu. 2018. "Evaluating the Performance of Sentinel-2, Landsat 8 and Pléiades-1 in Mapping Mangrove Extent and Species." Remote Sensing 10, no. 9: 1468.

Journal article
Published: 21 August 2017 in Remote Sensing
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The accurate and timely monitoring of regional urban extent is helpful for analyzing urban sprawl and studying environmental issues related to urbanization. This paper proposes a classification scheme for large-scale urban extent mapping by combining the Day/Night Band of the Visible Infrared Imaging Radiometer Suite on the Suomi National Polar-orbiting Partnership Satellite (NPP-VIIRS DNB) and the Normalized Difference Vegetation Index from the Moderate Resolution Imaging Spectroradiometer products (MODIS NDVI). A Back Propagation (BP) neural network based one-class classification method, the Present-Unlabeled Learning (PUL) algorithm, is employed to classify images into urban and non-urban areas. Experiments are conducted in mainland China (excluding surrounding islands) to detect urban areas in 2012. Results show that the proposed model can successfully map urban area with a kappa of 0.842 on the pixel level. Most of the urban areas are identified with a producer’s accuracy of 79.63%, and only 10.42% the generated urban areas are misclassified with a user’s accuracy of 89.58%. At the city level, among 647 cities, only four county-level cities are omitted. To evaluate the effectiveness of the proposed scheme, three contrastive analyses are conducted: (1) comparing the urban map obtained in this paper with that generated by the Defense Meteorological Satellite Program/Operational Linescan System Nighttime Light Data (DMSP/OLS NLD) and MODIS NDVI and with that extracted from MCD12Q1 in MODIS products; (2) comparing the performance of the integration of NPP-VIIRS DNB and MODIS NDVI with single input data; and (3) comparing the classification method used in this paper (PUL) with a linear method (Large-scale Impervious Surface Index (LISI)). According to our analyses, the proposed classification scheme shows great potential to map regional urban extents in an effective and efficient manner.

ACS Style

Run Wang; Bo Wan; Qinghua Guo; Maosheng Hu; Shunping Zhou. Mapping Regional Urban Extent Using NPP-VIIRS DNB and MODIS NDVI Data. Remote Sensing 2017, 9, 862 .

AMA Style

Run Wang, Bo Wan, Qinghua Guo, Maosheng Hu, Shunping Zhou. Mapping Regional Urban Extent Using NPP-VIIRS DNB and MODIS NDVI Data. Remote Sensing. 2017; 9 (8):862.

Chicago/Turabian Style

Run Wang; Bo Wan; Qinghua Guo; Maosheng Hu; Shunping Zhou. 2017. "Mapping Regional Urban Extent Using NPP-VIIRS DNB and MODIS NDVI Data." Remote Sensing 9, no. 8: 862.

Journal article
Published: 16 December 2014 in The Open Automation and Control Systems Journal
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ACS Style

Lin Yang; Zejun Zuo; Run Wang; Yaqin Ye; Maosheng Hu. Matching Road Network Combining Hierarchical Strokes and Probabilistic Relaxation Method. The Open Automation and Control Systems Journal 2014, 6, 268 -276.

AMA Style

Lin Yang, Zejun Zuo, Run Wang, Yaqin Ye, Maosheng Hu. Matching Road Network Combining Hierarchical Strokes and Probabilistic Relaxation Method. The Open Automation and Control Systems Journal. 2014; 6 (1):268-276.

Chicago/Turabian Style

Lin Yang; Zejun Zuo; Run Wang; Yaqin Ye; Maosheng Hu. 2014. "Matching Road Network Combining Hierarchical Strokes and Probabilistic Relaxation Method." The Open Automation and Control Systems Journal 6, no. 1: 268-276.