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Crop classification from Sentinel-2 derived vegetation indices using ensemble learning

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Please use this identifier to cite or link to this item:http://hdl.handle.net/2115/71029

Title: Crop classification from Sentinel-2 derived vegetation indices using ensemble learning
Authors: Sonobe, Rei Browse this author →KAKEN DB
Yamaya, Yuki Browse this author
Tani, Hiroshi Browse this author →KAKEN DB
Wang, Xiufeng Browse this author →KAKEN DB
Kobayashi, Nobuyuki Browse this author
Mochizuki, Kan-ichiro Browse this author
Keywords: crop
random forests
Sentinel-2
stacking
support vector machine
vegetation index
Issue Date: 18-May-2018
Publisher: SPIE
Journal Title: The Journal of Applied Remote Sensing
Volume: 12
Issue: 2
Start Page: 026019
Publisher DOI: 10.1117/1.JRS.12.026019
Abstract: The identification and mapping of crops are important for estimating potential harvest as well as for agricultural field management. Optical remote sensing is one of the most attractive options because it offers vegetation indices and some data have been distributed free of charge. Especially, Sentinel-2A, which is equipped with a multispectral sensor (MSI) with blue, green, red and near-infrared-1 bands at 10 m; red edge 1 to 3, near-infrared-2 and shortwave infrared 1 and 2 at 20 m; and 3 atmospheric bands (Band 1, Band 9 and Band 10) at 60 m, offers some vegetation indices calculated to assess vegetation status. However, sufficient consideration has not been given to the potential of vegetation indices calculated from MSI data. Thus, 82 published indices were calculated and their importance were evaluated for classifying crop types. In this study, the two most common classification algorithms, random forests (RF) and support vector machine (SVM), were applied to conduct cropland classification from MSI data. Additionally, super learning was applied for more improvement, achieving overall accuracies of 90.2–92.2%. Of the two algorithms applied (RF and SVM), the accuracy of SVM was superior and 89.3-92.0% of overall accuracies were confirmed. Furthermore, stacking contributed to higher overall accuracies (90.2-92.2%) and significant differences were confirmed with the results of SVM and RF. Our results showed that vegetation indices had the greatest contributions in identifying specific crop types.
Rights: © 2018 Society of Photo-Optical Instrumentation Engineers(SPIE). One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited., Rei Sonobe, Yuki Yamaya, Hiroshi Tani, Xiufeng Wang, Nobuyuki Kobayashi, Kan-ichiro Mochizuki, “Crop classification from Sentinel-2 derived vegetation indices using ensemble learning,” The Journal of Applied Remote Sensing, 12(2), 026019, (2018); https://doi.org/10.1117/1.JRS.12.026019.
Type: article (author version)
URI: http://hdl.handle.net/2115/71029
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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