High-resolution hyperspectral imaging via matrix factorization

Kawakami, Rika; Matsushita, Yasuyuki; Wright, John; Ben-Ezra, Moshe; Tai, Yu-Wing; Ikeuchi, Katsushi

doi:10.1109/cvpr.2011.5995457

Cited by 298 publications

(211 citation statements)

References 26 publications

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“…They have also been reported to improve the performance in computer vision tasks, such as, tracking [23], segmentation [25], recognition [29] and document analysis [18]. However, contemporary hyperspectral imaging lacks severely in terms of spatial resolution [16], [13]. The problem stems from the fact that each spectral image acquired by a hyperspectral system corresponds to a very narrow spectral window.…”

Section: Introductionmentioning

confidence: 99%

“…More recently, matrix factorization has played an important role in enhancing the spatial resolution of the ground based and the remote sensing hyperspectral imaging systems ( [16], [13], [32], [34]). Kawakami et al [16] have proposed to fuse a high spatial resolution RGB image with a hyperspectral image by decomposing each of the two images into two factors and constructing the desired image from the complementary factors of the two decompositions.…”

Section: Related Workmentioning

confidence: 99%

“…Kawakami et al [16] have proposed to fuse a high spatial resolution RGB image with a hyperspectral image by decomposing each of the two images into two factors and constructing the desired image from the complementary factors of the two decompositions. A very similar technique has been used by Huang et al [13] for remote sensing data.…”

Section: Related Workmentioning

confidence: 99%

“…The spectral characteristics of the resulting representation have proven critical in numerous applications, ranging from remote sensing [6], [3] to medical imaging [16]. They have also been reported to improve the performance in computer vision tasks, such as, tracking [23], segmentation [25], recognition [29] and document analysis [18].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Sparse Spatio-spectral Representation for Hyperspectral Image Super-resolution

Akhtar

Shafait

Mian

2014

Lecture Notes in Computer Science

228

179

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Abstract. Existing hyperspectral imaging systems produce low spatial resolution images due to hardware constraints. We propose a sparse representation based approach for hyperspectral image super-resolution. The proposed approach first extracts distinct reflectance spectra of the scene from the available hyperspectral image. Then, the signal sparsity, non-negativity and the spatial structure in the scene are exploited to explain a high-spatial but low-spectral resolution image of the same scene in terms of the extracted spectra. This is done by learning a sparse code with an algorithm G-SOMP+. Finally, the learned sparse code is used with the extracted scene spectra to estimate the super-resolution hyperspectral image. Comparison of the proposed approach with the state-ofthe-art methods on both ground-based and remotely-sensed public hyperspectral image databases shows that the presented method achieves the lowest error rate on all test images in the three datasets.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Sparse Spatio-spectral Representation for Hyperspectral Image Super-resolution

Akhtar

Shafait

Mian

2014

Lecture Notes in Computer Science

228

179

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show abstract

“…Among the recent advances in multispectral imaging Kawakami et al [25] combined a high spatial resolution RGB sensor with a low resolution MS camera to obtain high spatial resolution spectral images. The results are promising though the method produces some errors and is thus not mature enough to be used for the analysis of cultural heritage object.…”

mentioning

confidence: 99%

Integration of 3D and multispectral data for cultural heritage applications: Survey and perspectives

Chane

Mansouri

Marzani

et al. 2013

Image and Vision Computing

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Cultural heritage is increasingly put through imaging systems such as multispectral cameras and 3D scanners. Though these acquisition systems are often used independently, they collect complementary information (spectral vs. spatial) used for the study, archiving and visualization of cultural heritage. Recording 3D and multispectral data in a single coordinate system enhances the potential insights in data analysis.We present the state of the art of such acquisition systems and their applications for the study of cultural heritage. We also describe existing registration techniques that can be used to obtain 3D models with multispectral texture and explore the idea of optically tracking acquisition systems to ensure an easy and precise registration.

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Joint Camera Spectral Sensitivity Selection and Hyperspectral Image Recovery

Zhang

Zheng

et al. 2018

Lecture Notes in Computer Science

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High-resolution hyperspectral imaging via matrix factorization

Cited by 298 publications

References 26 publications

Sparse Spatio-spectral Representation for Hyperspectral Image Super-resolution

Sparse Spatio-spectral Representation for Hyperspectral Image Super-resolution

Integration of 3D and multispectral data for cultural heritage applications: Survey and perspectives

Joint Camera Spectral Sensitivity Selection and Hyperspectral Image Recovery

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