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Proceedings Paper

Multispectral and SAR image fusion based on wavelet and IHS transform
Author(s): Ling Liu; Ning Shu
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Paper Abstract

Multi-spectral images are of narrow-banded and high spectral resolution, however the transmission energy is low, which result in a large scanning IFOV and a loss of spatial resolution; while SAR (Synthetic Aperture Radar) images, as the angle reflection of buildings and low backscattering of waters, have excellent performance on texture structure and water bodies[1]. Therefore, adopting an appropriate fusion algorithm could obtain more accurate and abundant information than any a single data. Wavelet and IHS transform are complementary. An improved algorithm based on them applied to multi-source image fusion is presented, which could overcome the disadvantages that classical algorithms have such as inconspicuous improvement of space resolution, low level of information integration and serious spectral distortion. Intensity component is first extracted from the multi-spectral image by IHS transform, and then I component and SAR image are decomposed respectively by selected wavelet filter and decomposition layer. The modulation factor is gained through regional energy measurement in sub-windows for the new I-component. Finally the fused image could be acquired through inverse IHS transform. With different wavelet filters and decomposition layers, parameters are eventually fitted on Coiflets for four layers with subjective and objective indicator criteria. Through regional energy fusion, we could divide the smooth areas and marginal areas of the image in frequency domain, which could make a significative feature measurement in smaller ranges. Experimental results indicate that this model would achieve an excellent effect on the maintenance of spectral information in multi-spectral images as well as texture and edge in SAR images.

Paper Details

Date Published: 15 October 2009
PDF: 11 pages
Proc. SPIE 7492, International Symposium on Spatial Analysis, Spatial-Temporal Data Modeling, and Data Mining, 74922D (15 October 2009); doi: 10.1117/12.837403
Show Author Affiliations
Ling Liu, Wuhan Univ. (China)
Ning Shu, Wuhan Univ. (China)

Published in SPIE Proceedings Vol. 7492:
International Symposium on Spatial Analysis, Spatial-Temporal Data Modeling, and Data Mining
Yaolin Liu; Xinming Tang, Editor(s)

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