Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/224500
Title: Structured Sparse Coding-Based Hyperspectral Imagery Denoising With Intracluster Filtering
Authors: Wei Wei;Lei Zhang;Chunna Tian;Antonio Plaza;Yanning Zhang
Year: 2017
Publisher: IEEE
Abstract: Sparse coding can exploit the intrinsic sparsity of hyperspectral images (HSIs) by representing it as a group of sparse codes. This strategy has been shown to be effective for HSI denoising. However, how to effectively exploit the structural information within the sparse codes (structured sparsity) has not been widely studied. In this paper, we propose a new method for HSI denoising, which uses structured sparse coding and intracluster filtering. First, due to the high spectral correlation, the HSI is represented as a group of sparse codes by projecting each spectral signature onto a given dictionary. Then, we cast the structured sparse coding into a covariance matrix estimation problem. A latent variable-based Bayesian framework is adopted to learn the covariance matrix, the sparse codes, and the noise level simultaneously from noisy observations. Although the considered strategy is able to perform denoising through accurately reconstructing spectral signatures, an inconsistent recovery of sparse codes may corrupt the spectral similarity in each spatial homogeneous cluster within the scene. To address this issue, an intracluster filtering scheme is further employed to restore the spectral similarity in each spatial cluster, which results in better denoising results. Our experimental results, conducted using both simulated and real HSIs, demonstrate that the proposed method outperforms several state-of-the-art denoising methods.
URI: http://localhost/handle/Hannan/224500
volume: 55
issue: 12
More Information: 6860,
6876
Appears in Collections:2017

Files in This Item:
File SizeFormat 
8014501.pdf6.59 MBAdobe PDF
Title: Structured Sparse Coding-Based Hyperspectral Imagery Denoising With Intracluster Filtering
Authors: Wei Wei;Lei Zhang;Chunna Tian;Antonio Plaza;Yanning Zhang
Year: 2017
Publisher: IEEE
Abstract: Sparse coding can exploit the intrinsic sparsity of hyperspectral images (HSIs) by representing it as a group of sparse codes. This strategy has been shown to be effective for HSI denoising. However, how to effectively exploit the structural information within the sparse codes (structured sparsity) has not been widely studied. In this paper, we propose a new method for HSI denoising, which uses structured sparse coding and intracluster filtering. First, due to the high spectral correlation, the HSI is represented as a group of sparse codes by projecting each spectral signature onto a given dictionary. Then, we cast the structured sparse coding into a covariance matrix estimation problem. A latent variable-based Bayesian framework is adopted to learn the covariance matrix, the sparse codes, and the noise level simultaneously from noisy observations. Although the considered strategy is able to perform denoising through accurately reconstructing spectral signatures, an inconsistent recovery of sparse codes may corrupt the spectral similarity in each spatial homogeneous cluster within the scene. To address this issue, an intracluster filtering scheme is further employed to restore the spectral similarity in each spatial cluster, which results in better denoising results. Our experimental results, conducted using both simulated and real HSIs, demonstrate that the proposed method outperforms several state-of-the-art denoising methods.
URI: http://localhost/handle/Hannan/224500
volume: 55
issue: 12
More Information: 6860,
6876
Appears in Collections:2017

Files in This Item:
File SizeFormat 
8014501.pdf6.59 MBAdobe PDF
Title: Structured Sparse Coding-Based Hyperspectral Imagery Denoising With Intracluster Filtering
Authors: Wei Wei;Lei Zhang;Chunna Tian;Antonio Plaza;Yanning Zhang
Year: 2017
Publisher: IEEE
Abstract: Sparse coding can exploit the intrinsic sparsity of hyperspectral images (HSIs) by representing it as a group of sparse codes. This strategy has been shown to be effective for HSI denoising. However, how to effectively exploit the structural information within the sparse codes (structured sparsity) has not been widely studied. In this paper, we propose a new method for HSI denoising, which uses structured sparse coding and intracluster filtering. First, due to the high spectral correlation, the HSI is represented as a group of sparse codes by projecting each spectral signature onto a given dictionary. Then, we cast the structured sparse coding into a covariance matrix estimation problem. A latent variable-based Bayesian framework is adopted to learn the covariance matrix, the sparse codes, and the noise level simultaneously from noisy observations. Although the considered strategy is able to perform denoising through accurately reconstructing spectral signatures, an inconsistent recovery of sparse codes may corrupt the spectral similarity in each spatial homogeneous cluster within the scene. To address this issue, an intracluster filtering scheme is further employed to restore the spectral similarity in each spatial cluster, which results in better denoising results. Our experimental results, conducted using both simulated and real HSIs, demonstrate that the proposed method outperforms several state-of-the-art denoising methods.
URI: http://localhost/handle/Hannan/224500
volume: 55
issue: 12
More Information: 6860,
6876
Appears in Collections:2017

Files in This Item:
File SizeFormat 
8014501.pdf6.59 MBAdobe PDF