Rights statement: This is the author’s version of a work that was accepted for publication in Neurocomputing. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Neurocomputing, 330, 2019 DOI: 10.1016/j.neucom.2018.11.039
Accepted author manuscript, 5.79 MB, PDF document
Available under license: CC BY-NC-ND
Final published version
Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
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TY - JOUR
T1 - Hyperspectral image denoising via minimizing the partial sum of singular values and superpixel segmentation
AU - Liu, Y.
AU - Shan, C.
AU - Gao, Q.
AU - Gao, X.
AU - Han, J.
AU - Cui, R.
N1 - This is the author’s version of a work that was accepted for publication in Neurocomputing. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Neurocomputing, 330, 2019 DOI: 10.1016/j.neucom.2018.11.039
PY - 2019/2/22
Y1 - 2019/2/22
N2 - Hyperspectral images (HSIs) are often corrupted by noise during the acquisition process, thus degrading the HSI's discriminative capability significantly. Therefore, HSI denoising becomes an essential preprocess step before application. This paper proposes a new HSI denoising approach connecting Partial Sum of Singular Values (PSSV) and superpixels segmentation named as SS-PSSV, which can remove the noise effectively. Based on the fact that there is a high correlation between different bands of the same signal, it is easy to know the property of low rank between distinct bands. To this end, PSSV is utilized, and in order to better tap the low-rank attribute of pixels, we introduce the superpixels segmentation method, which allows pixels in HSI with high similarity to be grouped in the same sub-block as much as possible. Extensive experiments display that the proposed algorithm outperforms the state-of-the-art. © 2018 Elsevier B.V.
AB - Hyperspectral images (HSIs) are often corrupted by noise during the acquisition process, thus degrading the HSI's discriminative capability significantly. Therefore, HSI denoising becomes an essential preprocess step before application. This paper proposes a new HSI denoising approach connecting Partial Sum of Singular Values (PSSV) and superpixels segmentation named as SS-PSSV, which can remove the noise effectively. Based on the fact that there is a high correlation between different bands of the same signal, it is easy to know the property of low rank between distinct bands. To this end, PSSV is utilized, and in order to better tap the low-rank attribute of pixels, we introduce the superpixels segmentation method, which allows pixels in HSI with high similarity to be grouped in the same sub-block as much as possible. Extensive experiments display that the proposed algorithm outperforms the state-of-the-art. © 2018 Elsevier B.V.
KW - Denoising
KW - Hyperspectral images
KW - PSSV
KW - Superpixel segmentation
KW - Hyperspectral imaging
KW - Image segmentation
KW - Pixels
KW - Spectroscopy
KW - Superpixels
KW - Acquisition process
KW - De-noising
KW - Denoising approach
KW - Singular values
KW - State of the art
KW - Superpixel segmentations
KW - Superpixels segmentations
KW - Image denoising
KW - article
KW - noise
U2 - 10.1016/j.neucom.2018.11.039
DO - 10.1016/j.neucom.2018.11.039
M3 - Journal article
VL - 330
SP - 465
EP - 482
JO - Neurocomputing
JF - Neurocomputing
SN - 0925-2312
ER -