Subspace-Based Striping Noise Reduction in Hyperspectral Images

Acito, N.; Diani, Marco; Corsini, Giovanni

doi:10.1109/tgrs.2010.2081370

Cited by 93 publications

(36 citation statements)

References 29 publications

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“…This line by line scanning causes an artifact called striping noise which is often due to calibration errors and sensitivity variations of the detector [20]. Striping noise reduction (also referred to as destriping in the literature) for push-broom scanning techniques has been widely studied in the remote sensing literature [21,22] in particular for HSI [20,[23][24][25].…”

Section: Pattern Noisementioning

confidence: 99%

“…The noise model is given by N = N SI + N Str where N Str is the striping noise. N Str depends on the signal level and the position of detectors of the acquisition array in the cross-track direction (either i or j) [23]. In [20] a striping noise removal method was proposed by assuming that the striping noise contains higher spatial frequencies than the surface radiance.…”

Section: Mixed Signal Independent and Striping Noisesmentioning

confidence: 99%

“…A low-rank technique was proposed in [24] which uses a regularized cost function to preserve the spatial structures of subimages from spectral bands. In [23], a subspace-based approach is proposed to restore a HSI corrupted by striping noise and signal independent noise. The noise parameters were estimated by the least squares method.…”

Section: Mixed Signal Independent and Striping Noisesmentioning

confidence: 99%

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Noise Reduction in Hyperspectral Imagery: Overview and Application

et al. 2018

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Hyperspectral remote sensing is based on measuring the scattered and reflected electromagnetic signals from the Earth's surface emitted by the Sun. The received radiance at the sensor is usually degraded by atmospheric effects and instrumental (sensor) noises which include thermal (Johnson) noise, quantization noise, and shot (photon) noise. Noise reduction is often considered as a preprocessing step for hyperspectral imagery. In the past decade, hyperspectral noise reduction techniques have evolved substantially from two dimensional bandwise techniques to three dimensional ones, and varieties of low-rank methods have been forwarded to improve the signal to noise ratio of the observed data. Despite all the developments and advances, there is a lack of a comprehensive overview of these techniques and their impact on hyperspectral imagery applications. In this paper, we address the following two main issues; (1) Providing an overview of the techniques developed in the past decade for hyperspectral image noise reduction; (2) Discussing the performance of these techniques by applying them as a preprocessing step to improve a hyperspectral image analysis task, i.e., classification. Additionally, this paper discusses about the hyperspectral image modeling and denoising challenges. Furthermore, different noise types that exist in hyperspectral images have been described. The denoising experiments have confirmed the advantages of the use of low-rank denoising techniques compared to the other denoising techniques in terms of signal to noise ratio and spectral angle distance. In the classification experiments, classification accuracies have improved when denoising techniques have been applied as a preprocessing step.

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Section: Pattern Noisementioning

confidence: 99%

Section: Mixed Signal Independent and Striping Noisesmentioning

confidence: 99%

Section: Mixed Signal Independent and Striping Noisesmentioning

confidence: 99%

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Noise Reduction in Hyperspectral Imagery: Overview and Application

et al. 2018

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“…To remove stripes totally, we need to adjust the distribution of DNs to a reference one which is based on the similarity assumption of the data. The third kind of methods treats the destriping issue as an inverse problem [10][11][12][13][14][15]. An energy functional is formed by a regularization term and a fidelity term with a regularization parameter to balance the two terms.…”

Section: Introductionmentioning

confidence: 99%

A Unidirectional Total Variation and Second‐Order Total Variation Model for Destriping of Remote Sensing Images

Wang

Huang

Zhao

et al. 2017

Mathematical Problems in Engineering

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Remote sensing images often suffer from stripe noise, which greatly degrades the image quality. Destriping of remote sensing images is to recover a good image from the image containing stripe noise. Since the stripes in remote sensing images have a directional characteristic (horizontal or vertical), the unidirectional total variation has been used to consider the directional information and preserve the edges. The remote sensing image contaminated by heavy stripe noise always has large width stripes and the pixels in the stripes have low correlations with the true pixels. On this occasion, the destriping process can be viewed as inpainting the wide stripe domains. In many works, high-order total variation has been proved to be a powerful tool to inpainting wide domains. Therefore, in this paper, we propose a variational destriping model that combines unidirectional total variation and second-order total variation regularization to employ the directional information and handle the wide stripes. In particular, the split Bregman iteration method is employed to solve the proposed model. Experimental results demonstrate the effectiveness of the proposed method.

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“…They use the HSI to do the target detection [4] or classification [5] to find objects or materials of interest on the ground. Unfortunately, in the capturing procedure, the HSI is usually impaired by several types of noise, such as thermal noise [6], photonic noise [7], and strip noise [8]. Therefore, denoising methods [9][10][11][12][13] have become a critical step for improving the subsequent target detection and classification in remote sensing imaging applications [14].…”

Section: Introductionmentioning

confidence: 99%

Survey of hyperspectral image denoising methods based on tensor decompositions

Lin

Bourennane

2013

EURASIP J. Adv. Signal Process.

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A hyperspectral image (HSI) is always modeled as a three-dimensional tensor, with the first two dimensions indicating the spatial domain and the third dimension indicating the spectral domain. The classical matrix-based denoising methods require to rearrange the tensor into a matrix, then filter noise in the column space, and finally rebuild the tensor. To avoid the rearranging and rebuilding steps, the tensor-based denoising methods can be used to process the HSI directly by employing multilinear algebra. This paper presents a survey on three newly proposed HSI denoising methods and shows their performances in reducing noise. The first method is the Multiway Wiener Filter (MWF), which is an extension of the Wiener filter to data tensors, based on the TUCKER3 decomposition. The second one is the PARAFAC filter, which removes noise by truncating the lower rank K of the PARAFAC decomposition. And the third one is the combination of multidimensional wavelet packet transform (MWPT) and MWF (MWPT-MWF), which models each coefficient set as a tensor and then filters each tensor by applying MWF. MWPT-MWF has been proposed to preserve rare signals in the denoising process, which cannot be preserved well by using the MWF or PARAFAC filters. A real-world HYDICE HSI data is used in the experiments to assess these three tensor-based denoising methods, and the performances of each method are analyzed in two aspects: signal-to-noise ratio and improvement of subsequent target detection results. Review IntroductionHyperspectral images (HSI) attract more and more interest in recent years in different domains, such as geography, agriculture, and military [1][2][3]. They use the HSI to do the target detection [4] or classification [5] to find objects or materials of interest on the ground. Unfortunately, in the capturing procedure, the HSI is usually impaired by several types of noise, such as thermal noise [6], photonic noise [7], and strip noise [8]. Therefore, denoising methods [9-13] have become a critical step for improving the subsequent target detection and classification in remote sensing imaging applications [14].In HSI processing, images are modeled as a threedimensional tensor, i.e., two spatial dimensions and one spectral dimension. The classical denoising methods [15][16][17][18] rearrange the HSI into a matrix whose columns contain the spectral signatures of all the pixels, then estimate the signal subspace by methods based on the analysis *Correspondence: salah.bourennane@fresnel.fr Institut Fresnel/CNRS-UMR 7249 Ecole Centrale Marseille, Aix-Marseille Université, Marseille 13013, France of second-order statistics, and finally rebuild the original HSI structure after processing.Since matrix-based techniques cannot take advantage of spectra in hyperspectral images, therefore, in order to treat the HSI as a whole entity, some new techniques were developed. For example, an HSI was treated as a hypercube in order to take into account the correlation among different bands [19,20], tensor-algebra was brought to joi...

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Subspace-Based Striping Noise Reduction in Hyperspectral Images

Cited by 93 publications

References 29 publications

Noise Reduction in Hyperspectral Imagery: Overview and Application

Noise Reduction in Hyperspectral Imagery: Overview and Application

A Unidirectional Total Variation and Second‐Order Total Variation Model for Destriping of Remote Sensing Images

Survey of hyperspectral image denoising methods based on tensor decompositions

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