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23 November 2011 Multisource remote sensing image fusion based on curvelet and wavelet transform
Moyan Xiao, Zhibiao He
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Proceedings Volume 8006, MIPPR 2011: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications; 80060P (2011) https://doi.org/10.1117/12.901815
Event: Seventh International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2011), 2011, Guilin, China
Abstract
Aiming at limitations of existing multiresolution analysis (MRA) fusion methods, this paper proposes a new fusion method which combines curvelet and wavelet transform. Curvelet transform processes edges better than wavelet transform does. While wavelet transform handles smooth area better than curvelet transform does. As an image often includes more than one feature, the proposed method is conducted on the basis of region segmentation and use Àtrous wavelet transform (ATWT) to fuse smooth areas and fast discrete curvelet transform (FDCT) to fuse areas with edges. Furthermore, an optimal objective function defined based on a balance between spectral preservation and spatial resolution improvement is put forward to search optimal segmentation threshold. The optimal fusion result can be obtained by fusion processing through the optimal segmentation threshold. Landsat TM multispectral (MS) images and SPOT Panchromatic (Pan) image covering a region of Wuhan in Hubei province are tested to assess this proposed method. Visual evaluation and statistics analysis are employed to assess the quality of fused images of different methods. The proposed method demonstrates best results among methods being tested in this study. So by combining attributes of both transforms, it is possible to get better image fusion result than by using wavelet and curvelet individually.
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Moyan Xiao and Zhibiao He "Multisource remote sensing image fusion based on curvelet and wavelet transform", Proc. SPIE 8006, MIPPR 2011: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications, 80060P (23 November 2011); https://doi.org/10.1117/12.901815
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KEYWORDS
Image fusion
Image segmentation
Wavelet transforms
Image quality
Wavelets
Remote sensing
Image processing
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