Antarctic Ice-Sheet Freeze-Thaw Detection Based on Improved Physical Model

XIONG Zhangqiang; WANG Xingdong; LI Xinwu; LIANG Lei

Article Contents

Article Navigation > Chinese Journal of Electronics > 2014 > 23(1): 209-212

XIONG Zhangqiang, WANG Xingdong, LI Xinwu, et al., “Antarctic Ice-Sheet Freeze-Thaw Detection Based on Improved Physical Model,” Chinese Journal of Electronics, vol. 23, no. 1, pp. 209-212, 2014,

Citation:

XIONG Zhangqiang, WANG Xingdong, LI Xinwu, et al., “Antarctic Ice-Sheet Freeze-Thaw Detection Based on Improved Physical Model,” Chinese Journal of Electronics, vol. 23, no. 1, pp. 209-212, 2014,

XIONG Zhangqiang, WANG Xingdong, LI Xinwu, et al., “Antarctic Ice-Sheet Freeze-Thaw Detection Based on Improved Physical Model,” Chinese Journal of Electronics, vol. 23, no. 1, pp. 209-212, 2014,

Citation:

PDF( 226 KB)

Antarctic Ice-Sheet Freeze-Thaw Detection Based on Improved Physical Model

1.
Institute of Remote sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China;
2.
Key Laboratory of Metallogenic Prediction of Nonferrous Metals, Ministry of Education, School of Geosciences and Info-Physics, Central South University, Changsha 410083, China

Funds: This work is supported by the National Natural Science Foundation of China (No.41076129), the National High Technology Research and Development Program (863 Program) (No.2008AA121702).

More Information

Corresponding author: WANG Xingdong
Received Date: 2012-12-01
Rev Recd Date: 2013-04-01
Publish Date: 2014-01-05

Abstract

Abstract

On the basis of the simple ice-sheet freezethaw physical model, a new algorithm of Antarctic icesheet freeze-thaw detection was proposed for the automatic threshold segmentation, which did not depend on the priori freeze-thaw distribution. That was the histogram statistics for the data of ice-sheet freeze-thaw physical model by the use of generalized Gaussian model to automatically get the optimal threshold of the dry snow and the wet snow, so as to get the Antarctic freeze-thaw areas. The algorithm improves the computational efficiency, usability and operability of the ice-sheet freeze-thaw detection because the algorithm does not rely on the actual melt information and can automatically select many samples. To some extent, the algorithm also improves the accuracy of the ice-sheet freeze-thaw detection.
- Ice-sheet freeze-thaw detection,
- Microwave radiometer,
- Physical model,
- Generalized Gaussian model

FullText(HTML)

References(9)

References

M. Tedesco, W. Abdalati, H.J. Zwally,"Persistent surface snowmelt over Antarctica (1987-2006) from 19.35 GHz brightness temperatures", Geophysical Research Letters, Vol.34, No.18, pp.1-6, 2007.

M. Tedesco,"Assessment and development of snowmelt retrieval algorithms over Antarctica from K-band space borne brightness temperature (1979-2008)", Remote Sensing of Environment, Vol.113, No.5, pp.979-997, 2009.

X. Fettweis, J.P. Ypersele, H. Galle'e, et al.,"The 1979-2005 Greenland ice sheet melt extent from passive microwave data using an improved version of the melt retrieval XPGR algorithm", Geophysical Research Letters, Vol.34, No.5, pp.1-5, 2007.

W. Abdalati, K. Steffen,"Passive microwave-derived snow melt regions on the Greenland ice sheet", Geophysical Research Letters, Vol.22, No.7, pp.787-790, 1995.

K. Steffen, S.V. Nghiem, R. Huff, et al.,"The melt anomaly of 2002 on the Greenland ice sheet from active and passive microwave satellite observations", Geophysical Research Letters, Vol.31, No.20, pp.1-5, 2004.

I.S. Aschraft, D.G. Long,"Comparison of methods for melt detection over Greenland using active and passive microwave measurements", International Journal of Remote Sensing, Vol.27, No.12, pp.2469-2488, 2006.

X.D. Wang, Z.Q. Xiong, X.W. Li, et al.,"Antarctic ice-sheet freeze-thaw detection based on improved wavelet transform", Acta Electronica Sinica, Vol.41, No.2, pp.402-406, 2013.

Y. Bazi, L. Bruzzone, F. Melgani,"An unsupervised approach based on the generalized Gaussian model to automatic change detection in multi-temporal SAR images", IEEE Transactions on Geoscience and Remote Sensing, Vol.43, No.4, pp.874-887, 2005.

K. Sharifi, A. Leongarcia,"Estimation of shape parameter for generalized Gaussian distributions in subband decomposition of video", IEEE Transaction on Circuit and Systems for Video Technology, Vol.5, No.1, pp.52-56, 1995.

Relative Articles

Supplements(0)

Cited By

Proportional views