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Hokkaido University Collection of Scholarly and Academic Papers >
Graduate School of Agriculture / Faculty of Agriculture >
Peer-reviewed Journal Articles, etc >
Parameter tuning in the support vector machine and random forest and their performances in cross- and same-year crop classification using TerraSAR-X
| Title: | Parameter tuning in the support vector machine and random forest and their performances in cross- and same-year crop classification using TerraSAR-X |
| Authors: | Sonobe, Rei Browse this author | | Tani, Hiroshi Browse this author →KAKEN DB | | Wang, Xiufeng Browse this author →KAKEN DB | | Kobayashi, Nobuyuki Browse this author | | Shimamura, Hideki Browse this author |
| Keywords: | Classification | | machine learning | | TerraSAR-X | | gamma nought | | Hokkaido |
| Issue Date: | Nov-2014 |
| Publisher: | Taylor & Francis |
| Journal Title: | International Journal of Remote Sensing |
| Volume: | 35 |
| Issue: | 23 |
| Start Page: | 7898 |
| End Page: | 7909 |
| Publisher DOI: | 10.1080/01431161.2014.978038 |
| Abstract: | This article describes the comparison of three different classification algorithms for mapping crops in Hokkaido, Japan, using TerraSAR-X data. In the study area, beans, beets, grasslands, maize, potatoes, and winter wheat were cultivated. Although classification maps are required for both management and estimation of agricultural disaster compensation, those techniques have yet to be established. Some supervised learning models may allow accurate classification. Therefore, comparisons among the classification and regression tree (CART), the support vector machine (SVM), and random forests (RF) were performed. SVM was the optimum algorithm in this study, achieving an overall accuracy of 89.1% for the same-year classification, which is the classification using the training data in 2009 to classify the test data in 2009, and 78.0% for the cross-year classification, which is the classification using the training data in 2009 to classify the data in 2012. |
| Rights: | This is an Accepted Manuscript of an article published by Taylor & Francis in International Journal of Remote Sensing on 2014, available online: http://www.tandfonline.com/10.1080/01431161.2014.978038 |
| Type: | article (author version) |
| URI: | http://hdl.handle.net/2115/60345 |
| 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: 薗部 礼
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