Remote Sensing Applications and Decision Support

Super-resolution algorithm based on sparse representation and wavelet preprocessing for remote sensing imagery

[+] Author Affiliations
Ruizhi Ren

Jilin University, College of Electronic Science and Engineering, Changchun, Jilin, China

Jilin University, College of physics, Changchun, Jilin, China

Lingjia Gu

Jilin University, College of Electronic Science and Engineering, Changchun, Jilin, China

Beijing Normal University, State Key Laboratory of Remote Sensing Science, Beijing, China

Haoyang Fu, Chenglin Sun

Jilin University, College of physics, Changchun, Jilin, China

J. Appl. Remote Sens. 11(2), 026014 (May 10, 2017). doi:10.1117/1.JRS.11.026014
History: Received November 19, 2016; Accepted April 19, 2017
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Abstract.  An effective super-resolution (SR) algorithm is proposed for actual spectral remote sensing images based on sparse representation and wavelet preprocessing. The proposed SR algorithm mainly consists of dictionary training and image reconstruction. Wavelet preprocessing is used to establish four subbands, i.e., low frequency, horizontal, vertical, and diagonal high frequency, for an input image. As compared to the traditional approaches involving the direct training of image patches, the proposed approach focuses on the training of features derived from these four subbands. The proposed algorithm is verified using different spectral remote sensing images, e.g., moderate-resolution imaging spectroradiometer (MODIS) images with different bands, and the latest Chinese Jilin-1 satellite images with high spatial resolution. According to the visual experimental results obtained from the MODIS remote sensing data, the SR images using the proposed SR algorithm are superior to those using a conventional bicubic interpolation algorithm or traditional SR algorithms without preprocessing. Fusion algorithms, e.g., standard intensity-hue-saturation, principal component analysis, wavelet transform, and the proposed SR algorithms are utilized to merge the multispectral and panchromatic images acquired by the Jilin-1 satellite. The effectiveness of the proposed SR algorithm is assessed by parameters such as peak signal-to-noise ratio, structural similarity index, correlation coefficient, root-mean-square error, relative dimensionless global error in synthesis, relative average spectral error, spectral angle mapper, and the quality index Q4, and its performance is better than that of the standard image fusion algorithms.

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© 2017 Society of Photo-Optical Instrumentation Engineers

Citation

Ruizhi Ren ; Lingjia Gu ; Haoyang Fu and Chenglin Sun
"Super-resolution algorithm based on sparse representation and wavelet preprocessing for remote sensing imagery", J. Appl. Remote Sens. 11(2), 026014 (May 10, 2017). ; http://dx.doi.org/10.1117/1.JRS.11.026014


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