Linear spectral unmixing-based method including extended nonnegative matrix factorization for pan-sharpening multispectral remote sensing images

Moussa Sofiane Karoui

doi:10.1117/12.2028100

17 October 2013 Linear spectral unmixing-based method including extended nonnegative matrix factorization for pan-sharpening multispectral remote sensing images

Moussa Sofiane Karoui

Author Affiliations +

Proceedings Volume 8892, Image and Signal Processing for Remote Sensing XIX; 889205 (2013) https://doi.org/10.1117/12.2028100
Event: SPIE Remote Sensing, 2013, Dresden, Germany

Abstract

This paper presents a new fusion approach for pan-sharpening multispectral remote sensing images. This approach, related to Linear Spectral Unmixing (LSU) techniques, includes Extended Nonnegative Matrix Factorization (ExNMF) for combining low spatial resolution multispectral and high spatial resolution panchromatic data. ExNMF is applied to different real multispectral and panchromatic data sets with different spatial resolutions and different number of spectral bands. The quality of pan-sharpened multispectral images is evaluated by the jointly spectral and spatial Quality with No Reference (QNR) index. Obtained results show that our proposed method outperforms the Principal Component Analysis (PCA) and Gram-Schmidt (GS)-based standard literature methods.

Citation Download Citation

Moussa Sofiane Karoui "Linear spectral unmixing-based method including extended nonnegative matrix factorization for pan-sharpening multispectral remote sensing images", Proc. SPIE 8892, Image and Signal Processing for Remote Sensing XIX, 889205 (17 October 2013); https://doi.org/10.1117/12.2028100

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