https://scholars.lib.ntu.edu.tw/handle/123456789/425081
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. |
URI: | https://scholars.lib.ntu.edu.tw/handle/123456789/425081 | ISBN: | 9781538671504 | DOI: | https://api.elsevier.com/content/abstract/scopus_id/85064228885 10.1109/IGARSS.2018.8519099 |
Appears in Collections: | 土木工程學系 |
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