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Object-based crop classification in Hetao plain using random forest

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Abstract

Crop classification based on object-based image analysis (OBIA) is increasingly reported. However, it is still challenging to produce high-quality crop type maps by using recent techniques. This article introduces a new object-based crop classification algorithm which contains 4 steps. First, a random forest (RF) classifier is trained by using the initial training set, which tends to have a relatively small size. Second, importance scores for each feature variable are derived by using the RF model. Third, by treating the importance scores as weighting factors, a weighted Euclidean distance criterion is designed and used for sample creation to enlarge training set. Fourth, RF is re-trained by using the enlarged training set, and then it is employed for final classification. To validate the proposed strategy, a Worldview-2 image covering a part of Hetao plain is experimented. Results indicate that the new method yields the best overall accuracy, which equals 90.52%.

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Acknowledgements

This study is jointly supported by the National Natural Science Foundation of China under grant number of 61701265, and the Inner Mongolia Science Fund for Distinguished Young Scholars, under grant number of 2019JQ06. The anonymous reviewers are thanked for their constructive and helpful comments.

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Authors and Affiliations

  1. College of Water Conservancy and Civil Engineering, Inner Mongolia Agricultural University, No. 306, Zhaowuda Road, Hohhot, China

    Tengfei Su & Shengwei Zhang

  2. Inner Mongolia Autonomous Region Key Laboratory of Big Data Research and Application of Agriculture and Animal Husbandry, No. 306, Zhaowuda Road, Hohhot, China

    Shengwei Zhang

Authors
  1. Tengfei Su
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  2. Shengwei Zhang
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Correspondence to Shengwei Zhang.

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Communicated by: H. Babaie

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Su, T., Zhang, S. Object-based crop classification in Hetao plain using random forest. Earth Sci Inform 14, 119–131 (2021). https://doi.org/10.1007/s12145-020-00531-z

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