DocumentCode :
2885265
Title :
Backscattering change detection in SAR images using wavelet techniques
Author :
Bao, Mingquan
Author_Institution :
Center for Remote Imaging Sensing & Process., Nat. Univ. of Singapore, Singapore
Volume :
3
fYear :
1999
fDate :
1999
Firstpage :
1561
Abstract :
Backscattering change detection in synthetic aperture radar (SAR) is a challenging task, due to the presence of speckle noise. A 3-dimensional (2 spatial and 1 time dimension) wavelet shrinkage algorithm is developed to remove the noise. Compared to the often used 2-dimensional SAR wavelet shrinkage algorithm, this 3-dimensional algorithm exploits the correlation between SAR images, and therefore, a better signal to noise ratio is obtained. Furthermore, an autoregressive (AR) model is applied to analyze the temporal changes of radar backscattering. The change detection can be performed according to the AR model coefficients. Radarsat and ERS-2 SAR images are used to test this algorithm. The results show a significant improvement in the backscattering change detection
Keywords :
geophysical signal processing; geophysical techniques; image sequences; radar imaging; remote sensing by radar; synthetic aperture radar; terrain mapping; wavelet transforms; 3-dimensional algorithm; SAR; SAR image correlation; autoregressive model; backscattering; change detection; geophysical measurement technique; image processing; image sequence; land surface; radar imaging; radar remote sensing; radar scattering; speckle noise; synthetic aperture radar; temporal change; terrain mapping; wavelet; wavelet shrinkage algorithm; Backscatter; Change detection algorithms; Filters; Radar detection; Signal to noise ratio; Speckle; Synthetic aperture radar; Wavelet coefficients; Wavelet domain; Wavelet transforms;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Geoscience and Remote Sensing Symposium, 1999. IGARSS '99 Proceedings. IEEE 1999 International
Conference_Location :
Hamburg
Print_ISBN :
0-7803-5207-6
Type :
conf
DOI :
10.1109/IGARSS.1999.772019
Filename :
772019
Link To Document :
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