2017 Fiscal Year Final Research Report
Forest Investigation by Comprehensive Analysis of a Variety of Remote Sensing Data
| Project/Area Number |
15K18765
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| Research Category |
Grant-in-Aid for Young Scientists (B)
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| Allocation Type | Multi-year Fund |
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| Research Field |
Agricultural environmental engineering/Agricultural information engineering
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| Research Institution | Nihon University |
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Principal Investigator |
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| Project Period (FY) |
2015-04-01 – 2018-03-31
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| Keywords | 森林調査 / 樹種判別 / 地上型レーザスキャナ / 深層学習 |
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| Outline of Final Research Achievements |
We developed the method for automatic classification of individual tree species from laser scanned point cloud using deep learning. In our method, given a point cloud of an individual tree, point subset at breast height is selected, and then branches and leaves are removed by RANSAC-based circle fitting. Next the images are created which clearly represents bark texture using bi-cubic surface fitting or curvature estimation. These images are finally used in deep learning for species classification. From various experiments using point clouds of Japanese cedar and cypress trees within 15 meters from the scanned position, our method achieved high classification performance over 90%.
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| Free Research Field |
デジタル形状処理
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