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Title: CバンドSARデータを利用した機械学習アルゴリズムによる圃場の作物分類
Other Titles: Crop Classification by Machine Learning Algorithm Using C-band SAR Data
Authors: 山谷, 祐貴1 Browse this author
谷, 宏2 Browse this author →KAKEN DB
王, 秀峰3 Browse this author →KAKEN DB
薗部, 礼4 Browse this author
小林, 伸行5 Browse this author
望月, 貫一郎6 Browse this author
野田, 萌7 Browse this author
Authors(alt): YAMAYA, Yuki1
TANI, Hiroshi2
WANG, Xiufeng3
KOBAYASHI, Nobuyuki5
MOCHIZUKI, Kan-ichiro6
NODA, Megumi7
Issue Date: 8-Sep-2017
Publisher: 日本写真測量学会
Journal Title: 写真測量とリモートセンシング
Journal Title(alt): Journal of the Japan society of photogrammetry and remote sensing
Volume: 56
Issue: 4
Start Page: 143
End Page: 148
Publisher DOI: 10.4287/jsprs.56.143
Abstract: This paper presents crop classification using satellite data to establish a mapping method to replace the existing ground survey. We used five scenes of C-band fully polarimetric SAR satellite Radarsat-2 data. Datasets of sigma naught and four polarimetric parameters, Freeman-Durden (FD), Van Zyl (VZ), Yamaguchi (YG), and Cloude-Pottier (CP), were calculated from each image data. We assessed the accuracy of the classification obtained by the random forest machine learning algorithm. Three results are shown. First, the highest accuracy using only one of the five datasets (0.918) was obtained by the VZ parameter dataset. Second, using three datasets, the combination of the sigma naught, VZ parameter, and CP parameter datasets obtained the highest accuracy (0.922). Third, when we used all five datasets, the accuracy (0.918) was not improved. These results confirm that crop classification using Radarsat-2 C-band data is very effective and the use of a combination of sigma naught, VZ parameters, and CP parameters obtained the highest accuracy.
Rights: © 2017 一般社団法人 日本写真測量学会
© 2017 Japan Society of Photogrammetry and Remote Sensing
Type: article
Appears in Collections:農学院・農学研究院 (Graduate School of Agriculture / Faculty of Agriculture) > 雑誌発表論文等 (Peer-reviewed Journal Articles, etc)

Submitter: 山谷 祐貴

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