Land cover classification of finer resolution remote sensing data integrating temporal features from time series coarser resolution data

被引:97
作者
Jia, Kun [1 ,2 ]
Liang, Shunlin [1 ,2 ,3 ]
Zhang, Ning [4 ]
Wei, Xiangqin [5 ]
Gu, Xingfa [5 ]
Zhao, Xiang [1 ,2 ]
Yao, Yunjun [1 ,2 ]
Xie, Xianhong [1 ,2 ]
机构
[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
基金
国家高技术研究发展计划(863计划); 中国国家自然科学基金;
关键词
Land cover; Finer resolution; Temporal features; Classification; Landsat; 8; Fusion; MODIS SURFACE REFLECTANCE; SPATIAL-RESOLUTION; FUSION MODEL; CLIMATE; SENSORS; IMAGERY; CARBON; CHINA;
D O I
10.1016/j.isprsjprs.2014.04.004
中图分类号
P9 [自然地理学];
学科分类号
0705 ; 070501 ;
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.
引用
收藏
页码:49 / 55
页数:7
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