TY - GEN
T1 - Remote sensing image fusion based on fast discrete curvelet transform
AU - Ying, Li
AU - Xing, Xu
AU - Ben-Du, Bai
AU - Yan-Ning, Zhang
PY - 2008
Y1 - 2008
N2 - Wavelet transform has the good characteristic of spatial and frequency locality, but it isn't suitable for describing the signals, which have high dimensional singularities. Curvelet is one of new multiscale transform theories, which possess directionality and anisotropy, and it breaks some inherent limitations of wavelet in representing directions of edges in image. So when the curvelet transform is applied in image fusion, the characteristics of original images are taken better and implemented more easily. This paper tries fast discrete curvelet transform (FDCT) for image fusion of SAR (Synthetic Aperture Radar) image and TM (Thematic Mapper) image. Then, Visual result and statistical parameters are used to evaluate the result. The experimental results indicate that the FDCT-based fusion method can provide more detailed spatial information and simultaneously, preserves the richer spectral content than the conventional approach, such as the discrete wavelet transform (DWT) and the Intensity-Hue-Saturation (IHS) transform.
AB - Wavelet transform has the good characteristic of spatial and frequency locality, but it isn't suitable for describing the signals, which have high dimensional singularities. Curvelet is one of new multiscale transform theories, which possess directionality and anisotropy, and it breaks some inherent limitations of wavelet in representing directions of edges in image. So when the curvelet transform is applied in image fusion, the characteristics of original images are taken better and implemented more easily. This paper tries fast discrete curvelet transform (FDCT) for image fusion of SAR (Synthetic Aperture Radar) image and TM (Thematic Mapper) image. Then, Visual result and statistical parameters are used to evaluate the result. The experimental results indicate that the FDCT-based fusion method can provide more detailed spatial information and simultaneously, preserves the richer spectral content than the conventional approach, such as the discrete wavelet transform (DWT) and the Intensity-Hue-Saturation (IHS) transform.
KW - Fast discrete curvelet transform
KW - Image fusion
KW - Wavelet transform
UR - http://www.scopus.com/inward/record.url?scp=57849088660&partnerID=8YFLogxK
U2 - 10.1109/ICMLC.2008.4620387
DO - 10.1109/ICMLC.2008.4620387
M3 - 会议稿件
AN - SCOPUS:57849088660
SN - 9781424420964
T3 - Proceedings of the 7th International Conference on Machine Learning and Cybernetics, ICMLC
SP - 106
EP - 109
BT - Proceedings of the 7th International Conference on Machine Learning and Cybernetics, ICMLC
T2 - 7th International Conference on Machine Learning and Cybernetics, ICMLC
Y2 - 12 July 2008 through 15 July 2008
ER -