Multi-temporal remote sensing data applied in automatic land cover update using iterative training sample selection and Markov Random Field model |
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Authors: | Kun Jia Xiangqin Wei Lei Zhang Xin Du Yunjun Yao |
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Institution: | 1. State Key Laboratory of Remote Sensing Science, Research Center for Remote Sensing and GIS, and School of Geography, Beijing Normal University, Beijing, China;2. Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing, China;3. University of Chinese Academy of Sciences, Beijing, China |
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Abstract: | Automatic land cover update was an effective means to obtain objective and timely land cover maps without human disturbance. This study investigated the efficacy of multi-temporal remote sensing data and advanced non-parametric classifier on improving the classification accuracy of the automatic land cover update approach integrating iterative training sample selection and Markov Random Fields model when the historical remote sensing data were unavailable. The results indicated that two-temporal remote sensing data acquired in one crop growth season could significantly improve the classification accuracy of the automatic land cover update approach by approximately 3–4%. However, the support vector machine (SVM) classifier was not suitable to be integrated in the automatic land cover update approach, because the huge initially selected training samples made the training of the SVM classifier unrealizable. |
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Keywords: | land cover update automatic multi-temporal data Operational Land Imager |
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