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Multispectral image fusion by super pixel statistics

by   Nati Ofir, et al.

Multispectral image fusion is a fundamental problem of remote sensing and image processing. This problem is addressed by both classic and deep learning approaches. This paper is focused on the classic solutions and introduces a new novel approach to this family. The proposed method carries out multispectral image fusion based on the content of the fused images. It relies on analysis based on the level of information on segmented superpixels in the fused inputs. Specifically, I address the task of visible color RGB to Near-Infrared (NIR) fusion. The RGB image captures the color of the scene while the NIR captures details and sees beyond haze and clouds. Since each channel senses different information of the scene, their fusion is challenging and interesting. The proposed method is designed to produce a fusion that contains both advantages of each spectra. This manuscript experiments show that the proposed method is visually informative with respect to other classic fusion methods which can be run fastly on embedded devices with no need for heavy computation resources.


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