Comparison of crop classification capabilities of spaceborne multi-parameter SAR data

Xin Tian*, Erxue Chen, Zengyuan Li, Z. Bob Su, Feilong Ling, Lina Bai, Fengyu Wang

*Corresponding author for this work

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

2 Citations (Scopus)

Abstract

With the arisen spaceborne multi-parameter Synthetic Aperture Radar (SAR) systems, such as Envisat ASAR, TerraSAR-X, ALOS PALSAR, and RADARSAT-2, the interest of crop mapping has been increasing. The present study compares the capabilities of the multi-parameter SAR in discriminating the main crop types by object-based classification in Haian county of Jiangsu province, South China. Two kinds of information, SAR intensity based and SAR statistical properties based are used for Maximum Likelihood Classification (MLC) and Minimum Distance Classification (MDC) respectively. The results show that, the L-band SAR can uniquely identify mulberry from dryland crops, such as maize and vegetable and C-band SAR has some advantages in mapping rice. Specifically, the polarimetric RADARASAT-2 data can identify the rice with accuracy about 75% ∼ 80% which is similar as the result from X-band TerraSAR-X Spotlight data but higher than that from C-band dual-polarization Envisat ASAR data. Nevertheless, both of X- and C-band can hardly separate the mulberry from the other dry-land crops.

Original languageEnglish
Title of host publication2010 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2010
Place of PublicationHonolulu
PublisherIEEE
Pages359-362
Number of pages4
ISBN (Print)9781424495658, 9781424495665
DOIs
Publication statusPublished - 1 Dec 2010
Event30st IEEE International Geoscience And Remote Sensing Symposium, IGARSS 2010 - Honolulu, United States
Duration: 25 Jul 201030 Jul 2010
Conference number: 30

Publication series

NameInternational Geoscience and Remote Sensing Symposium (IGARSS)

Conference

Conference30st IEEE International Geoscience And Remote Sensing Symposium, IGARSS 2010
Abbreviated titleIGARSS 2010
CountryUnited States
CityHonolulu
Period25/07/1030/07/10

Keywords

  • Covariance matrix
  • Crop classification
  • Object based method
  • SAR
  • Statistical properties

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