A KPCA texture feature model for efficient segmentation of RADARSAT-2 SAR sea ice imagery-Reference-Cited by-同舟云学术

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A KPCA texture feature model for efficient segmentation of RADARSAT-2 SAR sea ice imagery

Published:2014-07-03 Issue:13 Volume:35 Page:5053-5072
ISSN:0143-1161
Container-title:International Journal of Remote Sensing
language:en
Short-container-title:International Journal of Remote Sensing

Author:

Xu Linlin¹,Li Jonathan¹²,Wong Alexander³,Wang Cheng²

Affiliation:

1. Department of Geography and Environmental Management, University of Waterloo, Waterloo, Ontario, Canada N2L 3G1

2. Key Laboratory for Underwater Acoustic Communication and Marine Information Technology, Xiamen University, Xiamen, Fujian 361005, China

3. Department of System Design Engineering, University of Waterloo, Waterloo, Ontario, Canada N2L 3G1

Publisher

Informa UK Limited

Subject

General Earth and Planetary Sciences

Link

https://www.tandfonline.com/doi/pdf/10.1080/01431161.2014.933279

Reference26 articles.

1. An investigation of the textural characteristics associated with gray level cooccurrence matrix statistical parameters

2. Status and future directions for sea ice remote sensing

3. Comparing Cooccurrence Probabilities and Markov Random Fields for Texture Analysis of SAR Sea Ice Imagery

4. Comparison and fusion of co‐occurrence, Gabor and MRF texture features for classification of SAR sea‐ice imagery

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