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Assessing the Suitability of Data from Sentinel-1A and 2A for Crop Classification
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 | classification | Hokkaido | machine learning | Sentinel-1A | Sentinel-2A |
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: http://www.tandfonline.com/doi/full/10.1080/15481603.2017.1351149. |
Type: | article (author version) |
URI: | http://hdl.handle.net/2115/71286 |
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)
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Submitter: 山谷 祐貴
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