https://scholars.lib.ntu.edu.tw/handle/123456789/425081
標題: | Pan-sharpen multispectral images using sparse representation | 作者: | PAI-HUI HSU Kuo, Hsiang-Lin |
關鍵字: | Dictionary learning | Pan-sharpening | Regularization | Sparse representation | Super resolution | 公開日期: | 31-十月-2018 | 卷: | 2018-July | 來源出版物: | International Geoscience and Remote Sensing Symposium (IGARSS) | 摘要: | © 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 |
顯示於: | 土木工程學系 |
在 IR 系統中的文件,除了特別指名其著作權條款之外,均受到著作權保護,並且保留所有的權利。