Crop Classification by Machine Learning Algorithm Using C-band SAR Data
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- YAMAYA Yuki
- 北海道大学大学院農学院
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- TANI Hiroshi
- 北海道大学大学院農学研究院
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- WANG Xiufeng
- 北海道大学大学院農学研究院
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- SONOBE Rei
- 静岡大学農学部
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- KOBAYASHI Nobuyuki
- 株式会社スマートリンク北海道
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- MOCHIZUKI Kan-ichiro
- 株式会社パスコ
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- NODA Megumi
- 札幌市
Bibliographic Information
- Other Title
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- 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
- Cバンド SAR データ オ リヨウ シタ キカイ ガクシュウ アルゴリズム ニ ヨル ホジョウ ノ サクモツ ブンルイ
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Abstract
<p>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.</p>
Journal
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- Journal of the Japan society of photogrammetry and remote sensing
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Journal of the Japan society of photogrammetry and remote sensing 56 (4), 143-148, 2017
Japan Society of Photogrammetry and Remote Sensing
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Details 詳細情報について
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- CRID
- 1390282763041750400
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- NII Article ID
- 130007479617
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- NII Book ID
- AN00111450
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- ISSN
- 18839061
- 02855844
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- HANDLE
- 2115/71450
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- NDL BIB ID
- 028534198
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- Text Lang
- ja
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- Data Source
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- JaLC
- IRDB
- NDL
- Crossref
- CiNii Articles
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- Abstract License Flag
- Disallowed