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A machine learning algorithm for automatic identification of cultivated land in remote sensing images |
Xun ZHOU1,2, Yuebin WANG3, Suhong LIU1,2( ), Peixin YU1,2, Xikai WANG1,2 |
1. School of Geography, Beijing Normal University, Beijing 100875, China
2. Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
3. School of Mathematical Sciences, Beijing Normal University, Beijing 100875, China |
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Abstract As an important kind of land resources, cultivated land is related to the country’s food security. So it is very significant to have a fast and accurate method for obtaining information of cultivated land. The traditional supervised classification methods of remote sensing image are based on the consistency of the spectral features or texture features between the training samples and the pixels/patches to be classified. These methods have strong dependence on training samples. This paper proposes an automatic classification algorithm of cultivated land based on the image window subarea. By using the machine learning algorithm, the automatic classification of cultivated land or non-cultivated land in the sub region of the image window can be realized by extracting the multi-spectral and multi-level features. Using this method, the unsupervised automatic classification of the type of the image window subarea is realized by establishing the feature database of the remote sensing image of the cultivated land in a certain area. With the high spatial resolution remote sensing image of Northeast China as an example, the experimental results show that the accuracy of the automatic classification algorithm is 90.8%. Being able to automatically acquire the cultivated land information, this method can also be used to extract any pure ground object from remote sensing images.
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| Keywords
image window subarea
feature database
machine learning
automatic identification of cultivated land
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Corresponding Authors:
Suhong LIU
E-mail: liush@bnu.edu.cn
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Issue Date: 07 December 2018
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2018,
Vol. 30
