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- 摘要
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Non-Convex Low-Rank Approximation for Hyperspectral Image Recovery with Weighted Total Varaition Regularization
摘要： Low-rank representation has been widely used as a powerful tool in hyperspectral image (HSI) recovery. The existing studies involving low-rank problems are commonly under the nuclear norm penalization. However, nuclear norm minimization tends to over-shrink the components of rank, which leads to modeling bias. In this paper, a new non-convex penalty is introduced to obtain an unbiased low-rank approximation. In Addition, local spatial neighborhood weighted spectral-spatial total variation (TV) regularization is introduced to preserve spatial structural information. And sparse 1l-norm is used as a constraint to sparse noise. Finally, a novel HSI non-convex low-rank relaxation restoration model is proposed. A number of experiments show that the proposed method can effectively remove the mixed-noise, and result in an unbiased estimate with better robustness.
关键词： Hyperspectral image(HSI),total variation(TV),low-rank representation,non-convex relaxation
更新于2025-09-10 09:29:36
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Hyperspectral Mixed Denoising Via Subspace Low Rank Learning and BM4D Filtering
摘要： This paper proposes a novel mixed noise removal method via subspace low rank representation and BM4D filtering for hyperspectral imagery (HSI). The proposed method is based on the following two facts. The first one is that the spectra in each class of HSI lie in different low-rank subspace, that is, the HSI data could be decomposed into two sub-matrices with lower ranks in the framework of subspace low rank representation. The second one is that the spatial structures of HSI have the property of non-local self-similarity (NSS), and the NSS could be effectively exploited by BM4D filter with no additional parameters. The proposed model can be easily and effectively solved by splitting it into several sub-problems via the alternating direction method of multipliers (ADMM). Experimental results validate that the proposed method outperforms other state-of-the-art denoising methods for HSI.
关键词： BM4D,subspace low rank representation,iterative learning,Hyperspectral mixed denoising,ADMM
更新于2025-09-09 09:28:46
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Spectral-Spatial Hyperspectral Image Classification via Locality and Structure Constrained Low-Rank Representation
摘要： Low-rank representation (LRR) has been applied widely in most fields due to its considerable ability to explore the low-dimensional subspace embedding in high-dimensional data. However, there are still some problems that LRR can’t effectively exploit the local structure and the representation for the given data is not discriminative enough. To tackle the above issues, we propose a novel locality and structure constrained low-rank representation (LSLRR) for hyperspectral image (HSI) classification. First, a distance metrics, which combines spectral and spatial similarity, is proposed to constrain the local structure. This makes two pixels in HSI with small distance have high similarity. Second, we exploit the classwise block-diagonal structure for the training data to learn the more discriminative representation for the testing data. And the experimental results verify the effectiveness and superiority of LSLRR comparing with other state-of-the-art methods.
关键词： low-rank representation,block-diagonal structure,hyperspectral image classification
更新于2025-09-09 09:28:46