NDLI: Application of Several Non-negative Matrix Factorization-Based Methods in Remote Sensing Image Fusion

Content Provider	IEEE Xplore Digital Library
Author	Zhi Huang Xianchuan Yu Guian Wang Zhongni Wang
Copyright Year	2008
Description	Author affiliation: Coll. of Inf. Sci. & Tech., Beijing Normal Univ., Beijing (Zhi Huang; Xianchuan Yu; Guian Wang; Zhongni Wang)
Abstract	Multi-spectral image keeps good multi-spectral feature in spite of its low special resolution, which canpsilat describe the earthpsilas surface information in detail. In contrast, panchromatic image keeps high special resolution but with limitations in spectral feature. The advantages of both multi-spectral image and panchromatic image can be integrated in the same image with fusion technique, so the fusion image with high quality will make it easier to visually evaluate or process by computers. In this paper, several new non-negative matrix factorization (NMF)-based fusion algorithms are presented for fusing multi-spectral image and panchromatic image, including NMF and principal component transform (PCA)-based fusion method (N-PCA), NMF and lifting wavelet transform (LWT)-based fusion method (N-LWT). The capabilities of each method are summarized. The experiments prove that NMF applied in the field of remote sensing image fusion can improve the quality of fusion image. A comparison of all proposed new fusion methods with PCA fusion method shows that the formers are better than the later, and the N-LWT fusion method is the best one among all methods.
Starting Page	29
Ending Page	33
File Size	361326
Page Count	5
File Format	PDF
ISBN	9780769533056
DOI	10.1109/FSKD.2008.439
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-10-18
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Wavelet transforms Image resolution Multispectral imaging Fuses Lifting Wavelet Transform Non-negative Matrix Factorization Sparse matrices Remote sensing Earth Image fussion Image fusion Principal component analysis Fuzzy systems
Content Type	Text
Resource Type	Article

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