文献类型：期刊文献

英文题名：The Performance of Airborne C-Band PolInSAR Data on Forest Growth Stage Types Classification

作者：Feng, Qi[1] Zhou, Liangjiang[1] Chen, Erxue[2] Liang, Xingdong[1] Zhao, Lei[2] Zhou, Yu[3]

第一作者：Feng, Qi

通信作者：Zhou, LJ[1]

机构：[1]Chinese Acad Sci, Inst Elect, Sci & Technol Microwave Imaging Lab, Beijing 100094, Peoples R China;[2]Chinese Acad Forestry, Res Inst Forest Resources Informat Tech, Beijing 100091, Peoples R China;[3]Clark Univ, Grad Sch Geog, Worcester, MA 01610 USA

年份：2017

卷号：9

期号：9

外文期刊名：REMOTE SENSING

收录：;EI(收录号：20173904204032);Scopus(收录号：2-s2.0-85029714748);WOS:【SCI-EXPANDED(收录号:WOS:000414138700085)】；

基金：This research was financially supported by Major National Science and Technology Infrastructure Program (Chinese Aeronautic Remote Sensing System). The authors would like to thank three reviewers for their helpful constructive comments that improved this research.

语种：英文

外文关键词：PolInSAR; forest types; polarimetric; texture; coherence

摘要：In this paper, we propose a classification scheme for forest growth stage types and other cover types using a support vector machine (SVM) based on the Polarimetric SAR Interferometric (PolInSAR) data acquired by Chinese Multidimensional Space Joint-observation SAR (MSJosSAR) system. Firstly, polarimetric, texture, and coherence features were calculated from the PolInSAR data. Secondly, the capabilities of the polarimetric, texture, and coherence features in land use/cover classification were quantified independently through histograms. Following this, the polarimetric features were used for the classification of land use/cover types, followed by a combination of texture and coherence features. Finally, the three classification results were validated against test samples using the confusion matrix. It was shown that, with the integration of texture and coherence features, the producer's accuracy for afforested land, young forest land, medium forest land, and near-mature forest land improved by 6%, 31%, 11%, and 6%, respectively, compared with the former experiment using solely polarimetric features. Our study indicates that the forest and non-forest lands can be discriminated by the polarimetric features, which also play an important role in the separation between afforested land and other forest types as well as medium forest land and near-mature forest land. The texture features further discriminate afforested land and other forest types, while the coherence features obviously improved the separation of young forest land and medium forest land. This paper provides an effective way of identifying various land use/cover types, especially for distinguishing forest growth stages with SAR data. It would be of great interest in regions with frequent cloud coverage and limited optical data for the monitoring of land use/cover types.