2024-03-29T09:59:43Zhttps://eprints.lib.hokudai.ac.jp/dspace-oai/requestoai:eprints.lib.hokudai.ac.jp:2115/603452022-11-17T02:08:08Zhdl_2115_20046hdl_2115_138Parameter tuning in the support vector machine and random forest and their performances in cross- and same-year crop classification using TerraSAR-XSonobe, ReiTani, HiroshiWang, XiufengKobayashi, NobuyukiShimamura, HidekiClassificationmachine learningTerraSAR-Xgamma noughtHokkaido519This 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.Taylor & FrancisJournal Articleapplication/pdfhttp://hdl.handle.net/2115/60345https://eprints.lib.hokudai.ac.jp/dspace/bitstream/2115/60345/1/manuscript_IJRS-2014.pdf0143-1161International Journal of Remote Sensing3523789879092014-11enginfo:doi/10.1080/01431161.2014.978038This 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/10.1080/01431161.2014.978038author