Title :
Compressive sensing of multispectral image based on PCA and Bregman split
Author :
Peng Liu ; Lingjun Zhao ; Yan Ma
Author_Institution :
Inst. of Remote Sensing & Digital Earth, Beijing, China
Abstract :
We reconstruct the multispectral image based on compressive sensing theory. Both spatial domain regularization and transform domain regularization are employed in the proposed objective function. Bregman split method is used to optimize the proposed objective function. In order to making use of the correlation features between different channels of multispectral image, principal component analysis (PCA) is introduced into the shrinkage step of the spatial domain regularization. For further enhance the performance of CS reconstruction, the similarity of wavelet coefficients between different channels are also explored in the shrinkage step of transform domain. We compare the proposed method with some other methods. Experiments validate the better performances of the proposed method, and it is attributed to combine two regularizations and employ the spectral correlation between channels.
Keywords :
compressed sensing; feature extraction; image reconstruction; principal component analysis; wavelet transforms; Bregman split method; CS reconstruction; PCA; compressive sensing theory; correlation features; multispectral image reconstruction; principal component analysis; shrinkage step; spatial domain regularization; transform domain regularization; wavelet coefficients; Compressed sensing; Correlation; Equations; Mathematical model; Principal component analysis; Remote sensing; Transforms; Compressive Sensing; Multispectral image;
Conference_Titel :
Geoscience and Remote Sensing Symposium (IGARSS), 2013 IEEE International
Conference_Location :
Melbourne, VIC
Print_ISBN :
978-1-4799-1114-1
DOI :
10.1109/IGARSS.2013.6723344