Eigen decomposition parameter based forest mapping using Radarsat-2 PolSAR data

Yang Li, Wen Hong, Fang Cao, Erxue Chen, David G. Goodenough, Hao Chen, Peng Wang, Ashlin Richardson

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

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, μ1 and μ2, 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.

Original languageEnglish
Title of host publication2010 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2010
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages4784-4787
Number of pages4
ISBN (Print)9781424495658, 9781424495665
DOIs
StatePublished - 2010
Externally publishedYes
Event2010 30th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2010 - Honolulu, United States
Duration: 25 Jul 201030 Jul 2010

Publication series

NameInternational Geoscience and Remote Sensing Symposium (IGARSS)

Conference

Conference2010 30th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2010
Country/TerritoryUnited States
CityHonolulu
Period25/07/1030/07/10

Keywords

  • Classification
  • Eigenvalue
  • Forest mapping
  • Polarimetric
  • Radarsat-2

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