Noise reduction of hyperspectral imagery using hybrid spatial-spectral derivative-domain wavelet shrinkage

By Hisham Othman, Shen-En Qian. IEEE Transactions on Geoscience and Remote Sensing, 2006

310 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/32a67dbb1b2f7c3e91f7ca60311abc9c8c20a4fc

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In Computer Vision and Pattern Recognition

In Media Technology

Citations in the corpus

Hyperspectral Image Restoration Using Low-Rank Matrix Recovery (2014, background): “Recently, researchers have paid most of their attention to the denoising methods that use the spatial and the spectral information together, and these approaches have made significant progress.”
Hyperspectral Image Restoration Via Total Variation Regularized Low-Rank Tensor Decomposition (2017, methodology): “D Yao Wang, Jiangjun Peng, Qian Zhao and Deyu Meng are with School of Mathematics and Statistics, Xi’an Jiaotong University, Xi’an 710049, China.”
Hyperspectral Image Denoising Employing a Spectral–Spatial Adaptive Total Variation Model (2012, methodology): “For example, Atkinson et al. [6] proposed a wavelet-based hyperspectral image denoising algorithm, and Othman and Qian [7] proposed a hybrid spatial–spectral derivative-domain wavelet shrinkage noise reduction (HSSNR) approach.”
Hyperspectral Subspace Identification (2008, methodology): “Independent component analysis [32], [33], projection pursuit [34], [35], and wavelet decomposition [36], [37] have also been considered.”
Noise Reduction in Hyperspectral Imagery: Overview and Application (2018, methodology): “In [43], a spatial-spectral HSI denoising approach was developed where a method of spectral derivation was proposed to concentrate the noise in the low frequencies, after which noise is removed by applying the 2D DWT in the spatial domain and the 1D DWT in the spectral domain.”
Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-Based Approaches (2012, unknown): “Independent component analysis [108], [109], projection pursuit [110], [111], and wavelet decomposition [112], [113] have also been considered.”
Total-Variation-Regularized Low-Rank Matrix Factorization for Hyperspectral Image Restoration (2016, background): “Othman and Qian [8] proposed a hybrid spatial spectral derivative-domain wavelet shrinkage model, which beneﬁts from the dissimilarity of the signal regularity in the spatial and spectral dimensions of the HSI.”