Affiliations: [a] School of Geographic Sciences, Xinyang Normal University, Xinyang, Henan, China | [b] Key Laboratory for Synergistic Prevention of Water and Soil Environmental Pollution, Xinyang Normal University, Xinyang, Henan, China | E-mail: [email protected]
Correspondence:
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Corresponding author: School of Geographic Sciences, Xinyang Normal University, Xinyang, Henan, China. E-mail: [email protected].
Abstract: With the development of space technology in recent years, various spacecraft with different sensors have been launched one after another, and there are more and more satellite remote sensing images in different situations. How to obtain better quality images has become the main research direction in the field of image fusion. Image fusion is an important branch and main research object of information fusion. Generally speaking, with the rapid development of the information society, people have higher and higher quality requirements for a variety of images, so There are serious challenges in storing, transmission and signal sampling. Nowadays, with the development of compressed sensing (CS) theory, a new sampling method has been extensively studied by many scholars. Compared with traditional Nyquist sampling theorem, Compression sensing can respond to the original signal with fewer sampling points. This paper is introduced the fusion algorithm of satellite remote sensing image based on compressed sensing. Firstly, the basic theory of compressed perceptual discretization signal is introduced. Secondly, a sparse CS remote sensing image fusion algorithm based on wavelet transform is proposed. Finally, through simulation verification, comparing the widely used IHS fusion and PCA fusion image methods, the method in this paper can get higher correlation coefficient and lower interaction entropy and spectral distortion after fusion. Compared with other methods in this paper, the fusion image can carry more space information and the original image is more similar.
