Abstract
The spectral mismatch between a multispectral (MS) image and its corresponding panchromatic (PAN) image affects the pansharpening quality, especially for WorldView-2 data. To handle this problem, a pansharpening method based on graph regularized sparse coding (GRSC) and adaptive coupled dictionary is proposed in this paper. Firstly, the pansharpening process is divided into three tasks according to the degree of correlation among the MS and PAN channels and the relative spectral response of WorldView-2 sensor. Then, for each task, the image patch set from the MS channels is clustered into several subsets, and the sparse representation of each subset is estimated through the GRSC algorithm. Besides, an adaptive coupled dictionary pair for each task is constructed to effectively represent the subsets. Finally, the high-resolution image subsets for each task are obtained by multiplying the estimated sparse coefficient matrix by the corresponding dictionary. A variety of experiments are conducted on the WorldView-2 data, and the experimental results demonstrate that the proposed method achieves better performance than the existing pansharpening algorithms in both subjective analysis and objective evaluation.
Highlights
For several decades, a huge amount of remote sensing images, which are provided by optical satellites, played a crucial role in human tasks
To reduce the influence of spectral mismatch, this paper proposes a PS method to sharpen the WorldView-2 data via graph regularized sparse coding and adaptive coupled dictionary (GRSC-ACD)
The first impressive work based on SR was proposed by Li et al, which assumes that the HR MS image patches have a sparse representation in a dictionary that is constructed by image patches randomly sampled from the HR MS images acquired by “comparable” sensors [33]
Summary
A huge amount of remote sensing images, which are provided by optical satellites, played a crucial role in human tasks. The VO-based methods usually adopt the linear combination model as the spatial enhancement term under the assumption that the spectral range of the PAN image almost covers that of all the MS channels. These methods are not suitable for pansharpening the WorldView-2 data. In our earlier work [56], we firstly exploited the graph regularized sparse coding (GRSC) [57] algorithm into the pansharpening In this method, we only consider the four-band MS images; for the eightband MS image, due to the spectral mismatch, the dictionary learned from the PAN image may not be adequate to sparsely represent the MS image patches. PS methods, the SR theory, and the GRSC algorithm; the proposed GRSC-ACD method is presented in Section 3; Section 4 compares and analyzes the experimental results on degraded and real remote sensing data, and Section 5 concludes this article
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