Multi-temporal remote sensing data applied in automatic land cover update using iterative training sample selection and Markov Random Field model

作者:Jia, K (Jia, Kun)[ 1,2,3 ] ; Li, QZ (Li, Qiangzi)[ 4 ] ; Wei, XQ (Wei, Xiangqin)[ 4,5 ] ; Zhang, L (Zhang, Lei)[ 4 ] ; Du, X (Du, Xin)[ 4 ] ; Yao, YJ (Yao, Yunjun)[ 1,2,3 ] ; Wang, XX (Wang, Xiaoxia)[ 1,2,3 ]

GEOCARTO INTERNATIONAL

卷: 30  期: 8  页: 882-893

DOI: 10.1080/10106049.2014.997310

出版年: SEP 14 2015

摘要

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.

通讯作者地址: Li, QZ (通讯作者)

Chinese Acad Sci, Inst Remote Sensing & Digital Earth, Beijing, Peoples R China.

地址:

[ 1 ] Beijing Normal Univ, State Key Lab Remote Sensing Sci, Res Ctr Remote Sensing, Beijing 100875, Peoples R China

[ 2 ] Beijing Normal Univ, GIS, Beijing 100875, Peoples R China

[ 3 ] Beijing Normal Univ, Sch Geog, Beijing 100875, Peoples R China

[ 4 ] Chinese Acad Sci, Inst Remote Sensing & Digital Earth, Beijing, Peoples R China

[ 5 ] Univ Chinese Acad Sci, Beijing, Peoples R China