贾坤等:Land cover classification of finer resolution remote sensing data integrating temporal features from time series coarser resolution data
被阅读 1344 次
2014-08-22

Land cover classification of finer resolution remote sensing data integrating temporal features from time series coarser resolution data
作者:Jia, K (Jia, Kun)[ 1,2 ] ; Liang, SL (Liang, Shunlin)[ 1,2,3 ] ; Zhang, N (Zhang, Ning)[ 4 ] ; Wei, XQ (Wei, Xiangqin)[ 5 ] ; Gu, XF (Gu, Xingfa)[ 5 ] ; Zhao, X (Zhao, Xiang)[ 1,2 ] ; Yao, YJ (Yao, Yunjun)[ 1,2 ] ; Xie, XH (Xie, Xianhong)[ 1,2 ]
ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING
卷: 93  页: 49-55
DOI: 10.1016/j.isprsjprs.2014.04.004
出版年: JUL 2014

摘要
Land cover classification of finer resolution remote sensing data is always difficult to acquire high-frequency time series data which contains temporal features for improving classification accuracy. This paper proposed a method of land cover classification with finer resolution remote sensing data integrating temporal features extracted from time series coarser resolution data. The coarser resolution vegetation index data is first fused with finer resolution data to obtain time series finer resolution data. Temporal features are extracted from the fused data and added to improve classification accuracy. The result indicates that temporal features extracted from coarser resolution data have significant effect on improving classification accuracy of finer resolution data, especially for vegetation types. The overall classification accuracy is significantly improved approximately 4% from 90.4% to 94.6% and 89.0% to 93.7% for using Landsat 8 and Landsat 5 data, respectively. The user and producer accuracies for all land cover types have been improved. (C) 2014 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS) Published by Elsevier B.V. All rights reserved.

通讯作者地址: Wei, XQ (通讯作者)
Chinese Acad Sci, Inst Remote Sensing & Digital Earth, Beijing 100101, Peoples R China.
地址:
[ 1 ] Beijing Normal Univ, State Key Lab Remote Sensing Sci, Beijing 100875, Peoples R China
[ 2 ] Beijing Normal Univ, Coll Global Change & Earth Syst Sci, Beijing 100875, Peoples R China
[ 3 ] Univ Maryland, Dept Geog Sci, College Pk, MD 20742 USA
[ 4 ] Minist Housing & Urban Rural Dev Peoples Republ C, Ctr Remote Sensing Applicat, Beijing, Peoples R China
[ 5 ] Chinese Acad Sci, Inst Remote Sensing & Digital Earth, Beijing 100101, Peoples R China