Research on assessment and improvement method of remote sensing image reconstruction

Li Sun; Nian Hua; Yanbo Yu; Zhanping Zhao

doi:10.1117/12.2294516

12 January 2018 Research on assessment and improvement method of remote sensing image reconstruction

Li Sun, Nian Hua, Yanbo Yu, Zhanping Zhao

Proceedings Volume 10620, 2017 International Conference on Optical Instruments and Technology: Optoelectronic Imaging/Spectroscopy and Signal Processing Technology; 106200P (2018) https://doi.org/10.1117/12.2294516
Event: International Conference on Optical Instruments and Technology 2017, 2017, Beijing, China

Abstract

Remote sensing image quality assessment and improvement is an important part of image processing. Generally, the use of compressive sampling theory in remote sensing imaging system can compress images while sampling，which can improve efficiency. A method of two-dimensional principal component analysis（2DPCA） is proposed to reconstruct the remote sensing image to improve the quality of the compressed image in this paper, which contain the useful information of image and can restrain the noise. Then, remote sensing image quality influence factors are analyzed, and the evaluation parameters for quantitative evaluation are introduced. On this basis, the quality of the reconstructed images is evaluated and the different factors influence on the reconstruction is analyzed, providing meaningful referential data for enhancing the quality of remote sensing images. The experiment results show that evaluation results fit human visual feature, and the method proposed have good application value in the field of remote sensing image processing.

Citation Download Citation

Li Sun, Nian Hua, Yanbo Yu, and Zhanping Zhao "Research on assessment and improvement method of remote sensing image reconstruction", Proc. SPIE 10620, 2017 International Conference on Optical Instruments and Technology: Optoelectronic Imaging/Spectroscopy and Signal Processing Technology, 106200P (12 January 2018); https://doi.org/10.1117/12.2294516

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