文献类型：会议论文

英文题名：EIGEN DECOMPOSITION PARAMETER BASED FOREST MAPPING USING RADARSAT-2 POLSAR DATA

作者：Li, Yang[1,6] Hong, Wen[1,6] Cao, Fang[1,6] Chen, Erxue[2] Goodenough, David G.[3,4,5] Chen, Hao[3] Wang, Peng[1,6] Richardson, Ashlin[3]

第一作者：Li, Yang

通信作者：Li, Y[1]

机构：[1]Natl Key Lab Microwave Imaging Technol, Beijing 100190, Peoples R China;[2]Chinese Acad Forestry, Inst Forest Resources Informat Tech, Beijing, Peoples R China;[3]Nat Resources Canada, Pacific Forestry Ctr, Sidney, BC, Canada;[4]Univ Victoria, Dept Comp Sci, Victoria, BC, Canada;[5]Univ Victoria, Dept Comp Sci, Beijing 100080, Peoples R China;[6]Chinese Acad Sci, Inst Elect, Beijing 100864, Peoples R China

会议论文集：30th IEEE International Geoscience and Remote Sensing Symposium (IGARSS) on Remote Sensing - Global Vision for Local Action

会议日期：JUN 25-30, 2010

会议地点：Honolulu, HI

语种：英文

外文关键词：Forest mapping; Radarsat-2; Polarimetric; eigenvalue; classification

年份：2010

摘要：In this paper, a set of polarimetric eigenvalue and eigenvector based parameters, e. g. entropy and anisotropy, are investigated for forest application. The correlation terms of the eigenvectors, mu(1) and mu(2), are found to be better for forest mapping in both summer and winter using Radarsat-2 quad-polarimetric space borne SAR data. These are used to automatically identify forest class pixels from the volume scattering category of a Freeman-Durden Wishart unsupervised segmentation map. The algorithm scheme was developed and implemented using fully polarimetric Radarsat-2 SAR (PolSAR) data acquired in July and October and the validity was evaluated using the ground reference data created from SPOT5 K-clustering classification map.