Mr. Ji Zhang Profile

Ji Zhang

at Hohai Univ

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Proceedings Article | 24 November 2014 Paper

A multi-sensor image fusion algorithm based on region-based selection and IHS transform

Xinnan Fan, Ji Zhang, Min Li, Pengfei Shi, Bingbin Zheng, Xuewu Zhang, Zhixiang Yang

Proceedings Volume 9301, 93012Y (2014) https://doi.org/10.1117/12.2073105

KEYWORDS: Image fusion, Image segmentation, Synthetic aperture radar, Image processing, Image processing algorithms and systems, Wavelets, Remote sensing, Principal component analysis, Detection and tracking algorithms, Earth observing sensors

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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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