| Precise crop classification based on multi\|features from time\|series Landsat 8 OLI images and Random Forest Algorithm |
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| DOI：10.7606/j.issn81000-7601.2020.03.37 |
| Key Words: Random Forest Algorithm crops classification time\|series Landsat 8 OLI images variable importance Xinjiang |
| Author Name | Affiliation | | LIU Jie | Huaihe River Basin Meteorological Center, Hefei, Anhui 230031, China
Anhui Meteorological Observatory, Hefei, Anhui 230031, China | | LIU Jikai | College of Resource and Environment, Anhui Science and Technology University, Fengyang, Anhui 233100, China | | AN Jingjing | Huaihe River Basin Meteorological Center, Hefei, Anhui 230031, China
Anhui Meteorological Observatory, Hefei, Anhui 230031, China | | ZHANG Chao | College of Resource and Environment, Anhui Science and Technology University, Fengyang, Anhui 233100, China |
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| Abstract: |
| According to the 7-scene landsat 8 OLI data of the 2014-2015 growth season in Wensu County, Aksu region, Xinjiang, we extracted spectral features, texture features, vegetation index, and other high\|dimensional information, and constructed a classification model based on the Random Forest (RF) Algorithm. The influence of the number of important parameter trees k and the number of node splitting features m in the RF model on classification accuracy was analyzed, and GINI coefficient was calculated to evaluate the importance of all features to explore the best feature subset. In this study, parameter calibration and information redundancy elimination of the model were completed, and the precise classification of various crop types from the study area of Wensu County was realized. The classification performance of RF and other machine learning algorithms was compared and analyzed. The results showed that among the three characteristics of crop classification, the most important ones were mean of image texture, land surface water index (LSWI) closely related to crop moisture content, and spectral reflectance of short\|wave infrared, which were corresponding to two key time phases of crops in arid areas: growth period and sowing period. Moreover, the classification accuracy of RF was affected by the number of classification features. When the deletion amount of “feature” was less than 30% of the total feature number, the classification accuracy of RF model remains unchanged. When the deletion amount was more than 70%, the classification accuracy decline increased. Finally, compared with decision tree, support vector machine, naive Bayes, k-nearest neighbor, and other supervised classification algorithms, Random Forest Algorithm had advantages in both accuracy and efficiency of classification results. |
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