Graph-Regularized Low-Rank Representation for Destriping of Hyperspectral Images

By Xiaoqiang Lu, Yulong Wang, Yuan Yuan. IEEE Transactions on Geoscience and Remote Sensing, 2013

317 citations recorded by the source corpus.

Hyperspectral image destriping is a challenging and promising theme in remote sensing. Striping noise is a ubiquitous phenomenon in hyperspectral imagery, which may severely degrade the visual quality. A variety of methods have been proposed to effectively alleviate the effects of the striping noise. However, most of them fail to take full advantage of the high spectral correlation between the observation subimages in distinct bands and consider the local manifold structure of the hyperspectral data space. In order to remedy this drawback, in this paper, a novel graph-regularized low-rank representation (LRR) destriping algorithm is proposed by incorporating the LRR technique. To obtain desired destriping performance, two sides of performing destriping are included: 1) To exploit the high spectral correlation between the observation subimages in distinct bands, the technique of LRR is first utilized for destriping, and 2) to preserve the intrinsic local structure of the original hyperspectral data, the graph regularizer is incorporated in the objective function. The experimental results and quantitative analysis demonstrate that the proposed method can both remove striping noise and achieve cleaner and higher contrast reconstructed results.

Subfields: Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/c1fea21f75c43468c5c3a4e165bc705153057ab7

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Citations in the corpus

Ieee Transactions on Pattern Analysis and Machine Intelligence Laplacian Regularized Low-rank Representation and Its Applications (unknown): “It is worthy pointing out that Lu et al. [32] proposed a similar formulation in their work and they provided an algorithm for the corresponding optimization problem by the augmented Lagrange multiplier method (ALM), however, the issue of handling linear mapping over Z was missing, no mention on how…”
Robust Principal Component Analysis on Graphs (2015, background): “The main differences between our model (1) and the various state-of-the-art factorized PCA models [10, 24, 17, 11, 20] are, as summarized in Table 1, the following.”
Noise Reduction in Hyperspectral Imagery: Overview and Application (2018, methodology): “A low-rank technique was proposed in [24] which uses a regularized cost function to preserve the spatial structures of subimages from spectral bands.”