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Earth Science Informatics

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01.10.2020 | Research Article

Object-based crop classification in Hetao plain using random forest

verfasst von: Tengfei Su, Shengwei Zhang

Erschienen in: Earth Science Informatics | Ausgabe 1/2021

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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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Titel: Object-based crop classification in Hetao plain using random forest
verfasst von: Tengfei Su
Shengwei Zhang
Publikationsdatum: 01.10.2020
Verlag: Springer Berlin Heidelberg
Erschienen in: Earth Science Informatics / Ausgabe 1/2021
Print ISSN: 1865-0473
Elektronische ISSN: 1865-0481
DOI: https://doi.org/10.1007/s12145-020-00531-z

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