A Novel Algorithm for Satellite Images Fusion Based on Compressed Sensing and PCA

Abstract

This paper studies the image fusion of high-resolution panchromatic image and low-resolution multispectral image. Based on the classic fusion algorithms on remote sensing image fusion, the PCA (principal component analysis) transform, and discrete wavelet transform, we carry out in-depth research. The compressed sensing (CS) abandons the full sample and shifts the sampling of the signal to sampling information that greatly reduces the potential consumption of traditional signal acquisition and processing. We combine compressed sensing with satellite remote sensing image fusion algorithm and propose an innovative fusion algorithm (CS-FWT-PCA), in which the symmetric fractional B-spline wavelet acts as the sparse base. In the algorithm we use Hama Da matrix as the measurement matrix and SAMP as the reconstruction algorithm and adopt an improved fusion rule based on the local variance. The simulation results show that the CS-FWT-PCA fusion algorithm achieves better fusion effect than the traditional fusion method.

Image Fusion in Remote Sensing Applications: A Review

“…The first principle component which contains the most information of the image resembles with the panchromatic image and hence is substituted by the panchromatic data. Finally the inverse principal component transform is done to get the new RGB bands of sharpened multi-spectral image from the principle components [38].…”

LDA Feature Selection for Satellite Image Fusion in HAAR Wavelet

“…Both PCA and Sparse fusion have specific advantages and disadvantages. PCA fusion will enhance the spatial quality but have dense nonzero entries that might represent uninformative features [3] [5]. Sparse fusion preserves important information but high spatial resolution is lacking.…”

“…This algorithm also overcomes the disadvantages of both PCA and Sparse representation. The effectiveness of proposed method by comparing its results with PCA and Sparse Fusion is demonstrated [5].…”

“…Commons Attribution CC BY high and the performance of multi-sensor system is improved. Accompanied image fusion schemes at three different levels[3][5].…”

Sparsity-regularized image reconstruction of decomposed K-edge data in spectral CT

“…Two choices for the penalty term in the cost function are investigated: a total variation (TV) penalty and a weighted sum of a TV penalty and an ℓ 1 -norm with a wavelet sparsifying transform (Lustig, Donoho & Pauly 2007, Dutta, Ahn, Li, Cherry & Leahy 2012). While TV and other sparsity promoting regularization strategies have been extensively applied for reconstruction problems that explictly or implicitly minimize a penalized least squares (PLS) cost function (Sidky & Pan 2008, Bian, Siewerdsen, Han, Sidky, Prince, Pelizzari & Pan 2010, Gao, Yu, Osher & Wang 2011, Xu, Yang, Tan & Anastasio 2012, Xu, Sidky, Pan, Stampanoni, Modregger & Anastasio 2012, Yang, Wang & Guo 2013), relatively few works have investigated the impact of exploiting such regularization strategies in combination with a statistically weighted data fidelity term in a PWLS framework (Ramani & Fessler 2012, Ma 2011). Computer-simulation and experimental phantom studies are conducted to visually and quantitatively demonstrate the efficacy of the proposed reconstruction methods.…”