The multi-sensor image fusion technology can obtain a more comprehensive and more accurate and reliable image, in
order to understand the scene or recognize the target more easily. However, most existing algorithms are mainly based on
optical remote sensing images, which is highly susceptible by media interference, supplemented by SAR images. The
image fusion between SAR images and PAN images also cannot save the textural feature and the color information
effectively at the same time. In view of these problems, this paper presents a multi-sensor image fusion algorithm based
on region-based selection and IHS transform. The SAR image and PAN image are firstly IHS transformed to achieve the
intensity (I), hue (H) and saturation (S) weights. The I weights of SAR image and PAN image are separately decomposed
using SIDWT algorithm to extract wavelet coefficients. Then, the I weight of SAR image is divided into regular area and
irregular area based on a new adaptive segmentation method. A new fusion rules is presented according to local feature,
and then used to fuse corresponding wavelet coefficients of the I weight of SAR image and PAN image. Inverse SIDWT
is carried out on the fused wavelet coefficients to get the I weight (I’) of fused image. Finally, the fused image is obtained
by inverse IHS transform of I’ weight with the H, S weight of PAN image. Experimental results of real images validated
the effectiveness of the proposed algorithm by objective evaluation such as standard deviation, entropy, average gradient,
etc.
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