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Hokkaido University Collection of Scholarly and Academic Papers >
Graduate School of Agriculture / Faculty of Agriculture >
北海道大学大学院農学研究院邦文紀要 = Memoirs of the Research Faculty of Agriculture, Hokkaido University >
Vol. 34, No. 2 >
TerraSAR-Xデータを使用したランダムフォレストによる作付作物の分類
| Title: | TerraSAR-Xデータを使用したランダムフォレストによる作付作物の分類 |
| Other Titles: | Crop classification by random forest using TerraSAR-X data |
| Authors: | 山谷, 祐貴1 Browse this author | | 薗部, 礼2 Browse this author | | 谷, 宏3 Browse this author →KAKEN DB | | 王, 秀峰4 Browse this author →KAKEN DB | | 小林, 伸行5 Browse this author | | 望月, 貫一郎6 Browse this author |
| Authors(alt): | Yamaya, Yuki1 | | Sonobe, Rei2 | | Tani, Hiroshi3 | | Wang, Xiufeng4 | | Kobayashi, Nobuyuki5 | | Mochizuki, Kan-ichiro6 |
| Keywords: | ランダムフォレスト |
| Issue Date: | 30-Mar-2017 |
| Publisher: | 北海道大学大学院農学研究院 |
| Journal Title: | 北海道大学大学院農学研究院邦文紀要 |
| Journal Title(alt): | Memories of the Research Faculty of Agriculture, Hokkaido University |
| Volume: | 34 |
| Issue: | 2 |
| Start Page: | 1 |
| End Page: | 11 |
| Abstract: | This paper presents crop classification using satellite data to establish a mapping method in place of the existing ground survey. We calculated four variables of sigma naught, and polarimetric parameters from TerraSAR-X HH-VV dual-polarization data, and assessed the accuracy of classification performed by machine learning algorithm “Random Forest”. The result showed about 90% of accuracy when we used five dates’ imagery and four variables, respectively. And accuracy assessment was done under the condition when the number of variables or scenes was reduced. The accuracy became worse when the number of variables was reduced, but it can be maintained when the number of dates was reduced, thus these results confirm that crop classification with the lower cost will become possible. |
| Type: | bulletin (article) |
| URI: | http://hdl.handle.net/2115/64961 |
| Appears in Collections: | 北海道大学大学院農学研究院邦文紀要 = Memoirs of the Research Faculty of Agriculture, Hokkaido University > Vol. 34, No. 2
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