Comparison of Remote Sensing Image Fusion Strategies Adopted in HSV and IHS
- 80 Downloads
It has been always a challenging task to keep an ideal balance of spectral and spatial resolution for merging panchromatic image and multispectral image. The mathematical theories such as color space transformation and Wavelet Packet Analysis are usually employed in information fusion area. Combining color space conversion with wavelet packet theory is a way of researching remote sensing image fusion algorithms further. In the paper, there are three existing image fusion strategies applied to the second layer of frequency bands decomposed by wavelet packet analysis in the HSV and the IHS (triangular coordinate) color space, respectively. Serial experiments demonstrate two core concepts. One is the effects of image fusion strategies based on region is super to those of fusion strategy based on pixel for the same color space; the other is the different performances are measured in the two color spaces. Specially, the space definition for image fused in the former color space is inferior to that in the latter color space; while the spectrum content for image fused in the former color space retains better than in the latter color space, when using the same fusion strategy in the two color space. As a result, application containing HSV space conversion can alleviate spectral deterioration, whereas fusion operation of IHS transformation can lift spatial definition.
KeywordsImage fusion Hue saturation value (HSV) Intensity hue saturation (IHS) Wavelet packet analysis (WPA) Three fusion strategies
This work is supported by National Natural Science Foundation of China (Grant No. 61461003).
- Bao, W. X., & Wang, P. (2011). Remote sensing image fusion based on wavelet packet analysis. IEEE 3rd International Conference on Communication Software and Networks, 2011, ICCSN (pp. 359–362).Google Scholar
- Ni, L. (2010). Wavelet transformation and image process. Hefei: China University of Science and Technolog Press.Google Scholar
- Sun, Y. (2005). Analysis and application of wavelet (Vol. 1, pp. 245–260). Beijing: China Machine Press.Google Scholar
- Sun, P., Deng, L., & Nie, J. (2012). A multi-scale remote sensing image fusion method based on wavelet decomposition. Application of Remote Sensing Technology, 27(6), 844–849.Google Scholar
- Zhang, Z., & Wang, Y. P. (2010). Digital image process and machine vision (pp. 233–269). Beijing: Posts and telecom press.Google Scholar
- Zuo, F., & Wan, P. S. (2011). Principle and pratice of digital image process (Vol. 1, pp. 34–59). Beijing: Publishing Hourse of Electronics Industry.Google Scholar