Dr. Jie Li Profile

Dr. Jie Li

at First Institute of Oceanography SOA

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SPIE Journal Paper | 10 August 2021

Object-based island green cover mapping by integrating UAV multispectral image and LiDAR data

Hao Liu, Pengfeng Xiao, Xueliang Zhang, Xinghua Zhou, Jie Li, Rui Guo

JARS, Vol. 15, Issue 03, 034512, (August 2021) https://doi.org/10.1117/12.10.1117/1.JRS.15.034512

KEYWORDS: Image segmentation, LIDAR, Associative arrays, Multispectral imaging, Unmanned aerial vehicles, Image classification, Iterated function systems, Feature selection, Data acquisition, Data modeling

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The unmanned aerial vehicle (UAV) plays an increasingly important role in monitoring and managing islands recently for their high feasibility and the miniaturization of sensors, which provide new possibilities for accurate island green cover mapping. We developed a framework that integrates UAV-acquired high-spatial resolution multispectral image and LiDAR data for effective object-based green cover mapping of Donkey Island in the Yellow Sea, China. LiDAR-derived structural and intensity information were combined with multispectral-derived spectral information for obtaining green cover objects. Five kinds of feature types [i.e., spectral, texture, height, intensity, and geometry features (GFs)] were calculated based on each object for green cover classification. Meanwhile, a multiple classifier system was adopted to improve the classification accuracy. The results indicate that the accuracy of green cover mapping could be significantly improved by the combination of multiple feature types. The inclusion of height and intensity features (IFs) can increase the overall classification accuracy by 7% and 5%, respectively, but the statistical significant differences are not found between these two feature types. The best green cover map is generated via a feature group obtained by the sequential backward selection with random forest method, reaching an overall accuracy of 88.5% and overall disagreement of 18.5%. Among the three major green cover classes, the accuracy of shrub class mapping improves the most when compared to classification using individual data, followed by tree and grass. Analysis of feature importance implies that spectral, height, and IFs are more beneficial to green cover mapping compared to texture and GFs. Furthermore, integrating multispectral and LiDAR data can provide more reliable green cover distribution maps and reduce the classification uncertainties.

SPIE Journal Paper | 26 August 2020

Object-based island hierarchical land cover classification using unmanned aerial vehicle multitype data

Hao Liu, Jie Li, Qiuhua Tang, Xinghua Zhou, Jiayuan Liu, Shuochong Shi, Bingzhi Huang, Wenxue Xu, Yanguang Fu

JARS, Vol. 14, Issue 03, 034514, (August 2020) https://doi.org/10.1117/12.10.1117/1.JRS.14.034514

KEYWORDS: RGB color model, Data modeling, Unmanned aerial vehicles, LIDAR, Image classification, Sensors, Roads, Image segmentation, Data acquisition, Classification systems

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The unmanned aerial vehicle (UAV) is an emerging technology applied recently in land cover classification, owing to its ability to acquire very high-resolution spatial data, that has provided an effective means for detailed land cover mapping, especially for a small island area. Selecting suitable UAV-acquired data and exploring the combined use of UAV multitype data are of significance for island mapping. Nine classification models were established through a fusion method of visible, multispectral, and light detection and ranging (LIDAR) data acquired by UAVs. A two-level hierarchical land cover classification (level 1 and level 2) of the Donkey Island in China was performed using geographic object-based image analysis with random forest classifier. We investigated the performance of land cover classification models containing different sets of features (spectral, height, intensity, and shape features extracted from UAV data) and evaluated the importance of various features. The results demonstrate that the overall accuracy (OA) of the models generally increase with decreasing detail and the amount of information entering the classification process. The OA achieved range from 82.08% to 92.54% and 74.12% to 85.08% across the nine models for level 1 and level 2, respectively. The best result was achieved with a model combining all features based on multispectral and LIDAR data. Height and intensity information input significantly affect the quality of classification models positively, with height apparently more significant than LIDAR information. When comparing different features, spectral features prominently assist in discriminating land cover classes. The importance of height and intensity features to classification accuracy varies for the classification models, showing greater importance in models based on visible data.

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