|Title:||Pan-sharpen multispectral images using sparse representation||Authors:||PAI-HUI HSU
Kuo, Hsiang Lin
|Keywords:||Dictionary learning | Pan-sharpening | Regularization | Sparse representation | Super resolution||Issue Date:||31-Oct-2018||Journal Volume:||2018-July||Source:||International Geoscience and Remote Sensing Symposium (IGARSS)||Abstract:||
© 2018 IEEE. Pan-sharpening is an image fusion technique of synthesizing a high-resolution multispectral image from a low-resolution multispectral image and a high-resolution panchromatic image. In this paper, a novel pan-sharpening method for remote sensing images has been proposed with sparse representation over learned dictionaries. In the proposed method, the dictionaries are learned only from the high-resolution panchromatic image via the joint learning algorithm, instead of learning from the high-resolution multispectral image which are not available in practice. The sparse coefficients of the panchromatic image and low-resolution panchromatic image are calculated by the orthogonal matching pursuit algorithm. Then, the fused high-resolution multispectral image can be constructed by combining the obtained sparse coefficients and the high-resolution dictionary. The experiment results indicate that the proposed method not only preserve spectral and spatial details of the source images but overcoming the drawbacks of fusion distortion.
|Appears in Collections:||土木工程學系|
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