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Remote Sens. 2015, 7(4), 4068-4091; doi:10.3390/rs70404068

Woodland Extraction from High-Resolution CASMSAR Data Based on Dempster-Shafer Evidence Theory Fusion

1
Chinese Academy of Surveying and Mapping, Lianhuachi West Road, No 28, Beijing 100830, China
2
National Quality Inspection and Testing Center for Surveying and Mapping Products, Lianhuachi West Road, No 28, Beijing 100830, China
3
Chinese University of Geosciences, Lumo Road, No 388, Wuhan 430074, China
*
Author to whom correspondence should be addressed.
Academic Editors: Nicolas Baghdadi and Prasad S. Thenkabail
Received: 17 September 2014 / Revised: 15 March 2015 / Accepted: 26 March 2015 / Published: 7 April 2015
(This article belongs to the Special Issue Remote Sensing Dedicated to Geographical Conditions Monitoring)
View Full-Text   |   Download PDF [28155 KB, uploaded 7 April 2015]   |  

Abstract

Mapping and monitoring of woodland resources is necessary, since woodland is vital for the natural environment and human survival. The intent of this paper is to propose a fusion scheme for woodland extraction with different frequency (P- and X-band) polarimetric synthetic aperture radar (PolSAR) and interferometric SAR (InSAR) data. In the study area of Hanjietou, China, a supervised complex Wishart classifier based on the initial polarimetric feature analysis was first applied to the PolSAR data and achieved an overall accuracy of 88%. An unsupervised classification based on elevation threshold segmentation was then applied to the InSAR data, with an overall accuracy of 90%. After Dempster-Shafer (D-S) evidence theory fusion processing for the PolSAR and InSAR classification results, the overall accuracy of fusion result reached 95%. It was found the proposed fusion method facilitates the reduction of polarimetric and interferometric SAR classification errors, and is suitable for the extraction of large areas of land cover with a uniform texture and height. The woodland extraction accuracy of the study area was sufficiently high (producer’s accuracy of 96% and user’s accuracy of 96%) enough that the woodland map generated from the fusion result can meet the demands of forest resource mapping and monitoring. View Full-Text
Keywords: woodland; PolSAR classification; InSAR segmentation; Dempster-Shafer evidence theory woodland; PolSAR classification; InSAR segmentation; Dempster-Shafer evidence theory
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Lu, L.; Xie, W.; Zhang, J.; Huang, G.; Li, Q.; Zhao, Z. Woodland Extraction from High-Resolution CASMSAR Data Based on Dempster-Shafer Evidence Theory Fusion. Remote Sens. 2015, 7, 4068-4091.

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