Study of Typical Arid Crops Classification Based on Machine Learning
HUANG Shuang-yan1, 2, YANG Liao1, CHEN Xi1*, YAO Yuan1, 2
1. Xinjiang Institute of Ecology and Geography，State Key Laboratory of Desert and Oasis Ecology，Chinese Academy of Sciences, Urumqi 830011，China
2. University of Chinese Academy of Sciences, Beijing 100049, China
Abstract：Accurate and timely crops classification information is of great significance for arid food security monitoring and ecological management. Adding sensitive waveband and improving classification methods are the major development trends of crops classification. In this paper, we carry out crop classification study based on Sentinel 2A time-series remote sensing data, and establish an object-oriented parcel point set in study area, trying to explore the influence of using different classification features on machine learning classification accuracy. Results indicate as follows: (1)Random forest classifier can effectively integrate the benefits of multidimensional vectors such as spectral or vegetation index, all the accuracy of different groups in this study are above 89%, while the supreme overall accuracy up to 94.02%. (2) The classification features extraction method, which was supported by object-oriented parcel point set, can resolve the issue of salt-and-pepper noise and fuzzy parcel boundary well. Meanwhile, it also improves the efficiency and accuracy of machine learning classifier, which can be demonstrated by the result that the classification accuracy of spectral group and index group increased by 3.13% and 4.07% respectively. (3)Red-edge features can help the classifier to capture the phenological differences and unique growth characteristics of different crops. And the introduction of the red-edge spectrum and red-edge index can improve the classification accuracy by 2.39% and 1.63% respectively, while the recognition ability of spring and winter wheat also improved significantly. The result of this study can be referred for the application of the machine learning method and the Sentinel 2A remote sensing data in arid agriculture remote sensing.
Key words：Machine learning; Random forest; Crop classification; Parcel data set; Red-edge
