NDLI: High-resolution hyperspectral imaging via matrix factorization

Content Provider	IEEE Xplore Digital Library
Author	Kawakami, R. Wright, J. Yu-Wing Tai Matsushita, Y. Ben-Ezra, M. Ikeuchi, K.
Copyright Year	2011
Description	Author affiliation: Microsoft Research Asia (Wright, J.; Yu-Wing Tai; Matsushita, Y.) \|\| The University of Tokyo (Kawakami, R.; Ikeuchi, K.) \|\| KAIST (Ben-Ezra, M.)
Abstract	Hyperspectral imaging is a promising tool for applications in geosensing, cultural heritage and beyond. However, compared to current RGB cameras, existing hyperspectral cameras are severely limited in spatial resolution. In this paper, we introduce a simple new technique for reconstructing a very high-resolution hyperspectral image from two readily obtained measurements: A lower-resolution hyper-spectral image and a high-resolution RGB image. Our approach is divided into two stages: We first apply an unmixing algorithm to the hyperspectral input, to estimate a basis representing reflectance spectra. We then use this representation in conjunction with the RGB input to produce the desired result. Our approach to unmixing is motivated by the spatial sparsity of the hyperspectral input, and casts the unmixing problem as the search for a factorization of the input into a basis and a set of maximally sparse coefficients. Experiments show that this simple approach performs reasonably well on both simulations and real data examples.
Starting Page	2329
Ending Page	2336
File Size	7624659
Page Count	8
File Format	PDF
ISBN	9781457703942
ISSN	10636919
e-ISBN	9781457703959
DOI	10.1109/CVPR.2011.5995457
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-06-20
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Hyperspectral imaging Cameras Materials Spatial resolution Image reconstruction
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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