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Assessing the Suitability of Data from Sentinel-1A and 2A for Crop Classification

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Title: Assessing the Suitability of Data from Sentinel-1A and 2A for Crop Classification
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: Agricultural fields
machine learning
Issue Date: 10-Jul-2017
Publisher: Taylor & Francis
Journal Title: GIScience & Remote Sensing
Volume: 54
Issue: 6
Start Page: 918
End Page: 938
Publisher DOI: 10.1080/15481603.2017.1351149
Abstract: Sentinel-1A C-SAR and Sentinel-2A MultiSpectral Instrument (MSI) provide data applicable to the remote identification of crop type. In this study, six crop types (beans, beetroot, grass, maize, potato, and winter wheat) were identified using five C-SAR images and one MSI image acquired during the 2016 growing season. To assess the potential for accurate crop classification with existing supervised learning models, the four different approaches of kernel-based extreme learning machine (KELM), multilayer feedforward neural networks, random forests, and support vector machine were compared. Algorithm hyperparameters were tuned using Bayesian optimization. Overall, KELM yielded the highest performance, achieving an overall classification accuracy of 96.8%. Evaluation of the sensitivity of classification models and relative importance of data types using data-based sensitivity analysis showed that the set of VV polarisation data acquired on 24 July (Sentinel-1A) and band 4 data (Sentinel-2A) had the greatest potential for use in crop classification.
Rights: This is an Accepted Manuscript of an article published by Taylor & Francis in GIScience & Remote Sensing on 10 Jul 2017, available online:
Type: article (author version)
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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