Comparison of Classification Algorithms and Training Sample Sizes in Urban Land Classification with Landsat Thematic Mapper Imagery
作者:Li, CC (Li, Congcong)[ 1 ] ; Wang, J (Wang, Jie)[ 2 ] ; Wang, L (Wang, Lei)[ 2 ] ; Hu, LY (Hu, Luanyun)[ 3 ] ; Gong, P (Gong, Peng)[ 2,3,4,5 ]
REMOTE SENSING
卷: 6  期: 2  页: 964-983
DOI: 10.3390/rs6020964
出版年: FEB 2014

摘要
Although a large number of new image classification algorithms have been developed, they are rarely tested with the same classification task. In this research, with the same Landsat Thematic Mapper (TM) data set and the same classification scheme over Guangzhou City, China, we tested two unsupervised and 13 supervised classification algorithms, including a number of machine learning algorithms that became popular in remote sensing during the past 20 years. Our analysis focused primarily on the spectral information provided by the TM data. We assessed all algorithms in a per-pixel classification decision experiment and all supervised algorithms in a segment-based experiment. We found that when sufficiently representative training samples were used, most algorithms performed reasonably well. Lack of training samples led to greater classification accuracy discrepancies than classification algorithms themselves. Some algorithms were more tolerable to insufficient (less representative) training samples than others. Many algorithms improved the overall accuracy padding: 8px 0px; word-break: normal; word-wrap: break-word; overflow: auto; text-align: justify; ">通讯作者地址: Gong, P (通讯作者)
Chinese Acad Sci, Inst Remote Sensing & Digital Earth, State Key Lab Remote Sensing Sci, Beijing 100101, Peoples R China.
地址:
[ 1 ] Beijing Normal Univ, State Key Lab Remote Sensing Sci, Beijing 100875, Peoples R China
[ 2 ] Chinese Acad Sci, Inst Remote Sensing & Digital Earth, State Key Lab Remote Sensing Sci, Beijing 100101, Peoples R China
[ 3 ] Tsinghua Univ, Ctr Earth Syst Sci, Minist Educ, Key Lab Earth Syst Modeling, Beijing 100084, Peoples R China
[ 4 ] Univ Calif Berkeley, Dept Environm Sci Policy & Management, Berkeley, CA 94720 USA
[ 5 ] Joint Ctr Global Change Studies, Beijing 100875, Peoples R China