文献类型：期刊文献

英文题名：Land Cover Classification Using Features Generated From Annual Time-Series Landsat Data

作者：Xiao, Jingge[1,2] Wu, Honggan[3] Wang, Chengbo[1] Xia, Hao[1]

第一作者：Xiao, Jingge

通信作者：Xiao, JG[1]

机构：[1]Chinese Acad Sci, Inst Remote Sensing & Digital Earth, Beijing 100101, Peoples R China;[2]Univ Chinese Acad Sci, Beijing 100049, Peoples R China;[3]Chinese Acad Forestry, Inst Forest Resource Informat Tech, Beijing 100089, Peoples R China

年份：2018

卷号：15

期号：5

起止页码：739-743

外文期刊名：IEEE GEOSCIENCE AND REMOTE SENSING LETTERS

收录：;EI(收录号：20181404982759);Scopus(收录号：2-s2.0-85044791109);WOS:【SCI-EXPANDED(收录号:WOS:000430730200021)】；

基金：This work was supported in part by the National Key Research and Development Program of China under Grant 2016YFB0502502 and in part by the China National Major Project of High-Resolution Earth Observation under Grant 21-Y30B05-9001-13/15.

语种：英文

外文关键词：Image classification; Landsat; LandUTime; time series; UniBagging

摘要：The objective of this letter is to investigate and demonstrate potential accuracy improvements in land cover classification using Landsat data, by integrating time-series features with a specially designed classification method. We present a new framework, mapping land cover types using annual time-series Landsat data (LandUTime), which adopts a two-stage approach. First, to generate pattern features, regression analysis is conducted on annual time-series Landsat data at the pixel level. Second, all features are packed into "blocks" and delivered to "UniBagging," which organizes base classifiers in separated sets according to the feature subspace, and conducts classification by integrating the results of many base classifiers. To evaluate the effectiveness of LandUTime, we performed a series of experiments on six Landsat bands. The results show marked improvements in both overall classification accuracy and Cohen's kappa coefficient. LandUTime is particularly promising for distinguishing objects whose spectral characteristics show temporal differences, such as deciduous or evergreen forest and cultivated crops.