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Peer-reviewed Journal Articles, etc >
Discrimination of crop types with TerraSAR-X-derived information
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| Title: | Discrimination of crop types with TerraSAR-X-derived information |
| Authors: | Sonobe, Rei Browse this author | | Tani, Hiroshi Browse this author →KAKEN DB | | Wang, Xiufeng Browse this author | | Kobayashi, Nobuyuki Browse this author | | Shimamura, Hideki Browse this author |
| Keywords: | Classification | | Random forest | | Support vector machine | | TerraSAR-X |
| Issue Date: | 13-Nov-2014 |
| Publisher: | Elsevier |
| Journal Title: | Physics and Chemistry of the Earth, Parts A/B/C |
| Volume: | 83-84 |
| Start Page: | 2 |
| End Page: | 13 |
| Publisher DOI: | 10.1016/j.pce.2014.11.001 |
| Abstract: | Although classification maps are required for management and for the estimation of agricultural disaster compensation, those techniques have yet to be established. This paper describes the comparison of three different classification algorithms for mapping crops in Hokkaido, Japan, using TerraSAR-X (including TanDEM-X) dual-polarimetric data. In the study area, beans, beets, grasslands, maize, potatoes and winter wheat were cultivated. In this study, classification using TerraSAR-X-derived information was performed. Coherence values, polarimetric parameters and gamma nought values were also obtained and evaluated regarding their usefulness in crop classification. Accurate classification may be possible with currently existing supervised learning models. A comparison between the classification and regression tree (CART), support vector machine (SVM) and random forests (RF) algorithms was performed. Even though J–M distances were lower than 1.0 on all TerraSAR-X acquisition days, good results were achieved (e.g., separability between winter wheat and grass) due to the characteristics of the machine learning algorithm. It was found that SVM performed best, achieving an overall accuracy of 95.0% based on the polarimetric parameters and gamma nought values for HH and VV polarizations. The misclassified fields were less than 100 a in area and 79.5–96.3% were less than 200 a with the exception of grassland. When some feature such as a road or windbreak forest is present in the TerraSAR-X data, the ratio of its extent to that of the field is relatively higher for the smaller fields, which leads to misclassifications. |
| Rights: | © 2014. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/ | | http://creativecommons.org/licenses/by-nc-nd/4.0/ |
| Type: | article (author version) |
| URI: | http://hdl.handle.net/2115/63532 |
| 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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