Title
Sea Ice Classification Using TerraSAR-X ScanSAR Data With Removal of Scalloping and Interscan Banding
Abstract
Effective methods of sea ice classification are crucial to regional ice type mapping using spaceborne synthetic aperture radar (SAR), especially in ScanSAR mode. However, in TerraSAR-X ScanSAR images of ocean scenes, the scalloping and interscan banding (ISB) artifacts are usually visible. Though these two artifacts could be reduced to a certain degree where residual artifacts do not impede the visual interpretation of SAR images, this is not the case when it comes to sea ice classification. The difficulty mainly comes from the involvement of texture features in sea ice classification. Texture features, especially gray level co-occurrence matrix (GLCM) are useful bases for sea ice classification using SAR data in single or dual polarization. When GLCM is applied to TerraSAR-X ScanSAR data, however, scalloping and ISB artifacts are found to be intensified, further affecting the classification result. How to largely eliminate the effects of scalloping and ISB on sea ice classification has not been studied thoroughly yet. In this paper, an approach combining Kalman filter, GLCM, and support vector machine is proposed. An independent testing shows that this approach is effective at the removal of scalloping and ISB's effects on sea ice classification using TerraSAR-X ScanSAR data, with the overall accuracy of 88.26%. Besides, convolutional neural network is implemented to compare with the proposed approach.
Year
DOI
Venue
2019
10.1109/JSTARS.2018.2889798
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Keywords
Field
DocType
Sea ice,Synthetic aperture radar,Backscatter,Kalman filters,Feature extraction,Earth
Residual,Sea ice,Convolutional neural network,Synthetic aperture radar,Backscatter,Remote sensing,Support vector machine,Feature extraction,Kalman filter,Mathematics
Journal
Volume
Issue
ISSN
12
2
1939-1404
Citations 
PageRank 
References 
0
0.34
0
Authors
6
Name
Order
Citations
PageRank
Lu Zhang100.34
Huiying Liu202.03
Xinwei Gu300.68
Huadong Guo445984.66
Jie Chen52312.76
Guang Liu646.36