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author:

Tian, X. (Tian, X..) [1] | Chen, E. (Chen, E..) [2] | Li, Z. (Li, Z..) [3] | Su, Z.B. (Su, Z.B..) [4] | Ling, F. (Ling, F..) [5] | Bai, L. (Bai, L..) [6] | Wang, F. (Wang, F..) [7]

Indexed by:

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. © 2010 IEEE.

Keyword:

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

Community:

  • [ 1 ] [Tian, X.]Chinese Academy of Forestry, Beijing, 100091, China
  • [ 2 ] [Tian, X.]Faculty of Geo-Information Science and Earth Observation, University of Twente, Enschede, 7500 AA, Netherlands
  • [ 3 ] [Chen, E.]Chinese Academy of Forestry, Beijing, 100091, China
  • [ 4 ] [Li, Z.]Chinese Academy of Forestry, Beijing, 100091, China
  • [ 5 ] [Su, Z.B.]Faculty of Geo-Information Science and Earth Observation, University of Twente, Enschede, 7500 AA, Netherlands
  • [ 6 ] [Ling, F.]Chinese Academy of Forestry, Beijing, 100091, China
  • [ 7 ] [Ling, F.]Fuzhou University, Fuzhou, 350002, China
  • [ 8 ] [Bai, L.]Chinese Academy of Forestry, Beijing, 100091, China
  • [ 9 ] [Wang, F.]Chinese Academy of Forestry, Beijing, 100091, China

Reprint 's Address:

  • [Tian, X.]Chinese Academy of Forestry, Beijing, 100091, China

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Source :

International Geoscience and Remote Sensing Symposium (IGARSS)

Year: 2010

Page: 359-362

Language: English

Cited Count:

WoS CC Cited Count:

SCOPUS Cited Count: 3

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 2

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