Remote sensing image fusion based on dictionary learning and sparse representation

Fei Yin; Shuhua Cao; Xiaojie Xu

doi:10.1117/12.2550316

27 November 2019 Remote sensing image fusion based on dictionary learning and sparse representation

Fei Yin, Shuhua Cao, Xiaojie Xu

Proceedings Volume 11321, 2019 International Conference on Image and Video Processing, and Artificial Intelligence; 113210Z (2019) https://doi.org/10.1117/12.2550316
Event: The Second International Conference on Image, Video Processing and Artifical Intelligence, 2019, Shanghai, China

Abstract

It is a tough challenge to find a remote sensing image fusion method which can acquire spatial and spectral information as much as possible from panchromatic (PAN) image and multispectral (MS) image. Sparse representation (SR) can realize remote sensing image fusion better than other popular methods, which is a powerful tool for dealing with the signals of high dimensionality. In addition, to gain better fusion results without color distortion, this paper propose a remote sensing image fusion algorithm with SR and color matching in stead of the intensity hue saturation (IHS) color model and Brovey transform. The experimental results show that proposed method can make fused image with both better spatial details and spectral information compared with three well-known methods.

Citation Download Citation

Fei Yin, Shuhua Cao, and Xiaojie Xu "Remote sensing image fusion based on dictionary learning and sparse representation", Proc. SPIE 11321, 2019 International Conference on Image and Video Processing, and Artificial Intelligence, 113210Z (27 November 2019); https://doi.org/10.1117/12.2550316

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