Pansharpening multispectral remote sensing data by multiplicative joint nonnegative matrix factorization
| Autor: | Moussa Sofiane Karoui, Issam Boukerch, Khelifa Djerriri |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
010504 meteorology & atmospheric sciences
business.industry Multiplicative function Multispectral image 0211 other engineering and technologies Pattern recognition 02 engineering and technology 01 natural sciences Unobservable Non-negative matrix factorization Image (mathematics) Panchromatic film Matrix decomposition Multispectral pattern recognition General Earth and Planetary Sciences Computer vision Artificial intelligence business 021101 geological & geomatics engineering 0105 earth and related environmental sciences Mathematics |
| Zdroj: | International Journal of Remote Sensing. 37:805-818 |
| ISSN: | 1366-5901 0143-1161 |
| DOI: | 10.1080/01431161.2015.1137650 |
| Popis: | Pansharpening aims at combining observable panchromatic and multispectral images to generate an unobservable image with the high spatial resolution of the former and the spectral diversity of the latter. In this paper a new fusion method is proposed. This method, related to linear spectral unmixing LSU techniques and based on non-negative matrix factorization NMF, optimizes, by new iterative–multiplicative update rules, a joint criterion that exploits a spatial degradation model between the two images. The proposed Multiplicative Joint Non-negative Matrix Factorization MJNMF approach is applied to synthetic and real data, and its effectiveness in spatial and spectral domains is evaluated with commonly used performance criteria. Experimental results show that the proposed method yields good spectral and spatial fidelities of the pansharpened data. Also, it outperforms those tested from the literature. |
| Databáze: | OpenAIRE |
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