Toward Optimal Destriping of MODIS Data Using a Unidirectional Variational Model

Bouali, Marouan; Ladjal, Saïd

doi:10.1109/tgrs.2011.2119399

Cited by 207 publications

(136 citation statements)

References 21 publications

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“…To validate the destriping performance, we performed simulations and actual experiments, and compared our method with the combined wavelet-Fourier filter (wavelet-FFT) [5], the variational stationary noise remover method (VSNR) [30], the SSAHTV method [14], and the UV method [10]. For fair comparison, parameters in these algorithms were kept equal for each band.…”

Section: Resultsmentioning

confidence: 99%

“…Motivated by this observation, the authors of Ref. [10] formulated an intuitive unidirectional variation (UV) model as ( )…”

Section: Problem Formulationmentioning

confidence: 99%

“…Though image statistical properties can be preserved, these equalization methods are highly sensitive to the assumption of statistical homogeneity. The third category is a variational approach [10][11][12]. The destriping issue is formulated as an inverse problem, and can be tackled in the regularization framework.…”

Section: Introductionmentioning

confidence: 99%

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Destripe Hyperspectral Images with Spectral-spatial Adaptive Unidirectional Variation and Sparse Representation

Zhou

Wang

Huo

et al. 2016

Journal of the Optical Society of Korea

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Hyperspectral images are often contaminated with stripe noise, which severely degrades the imaging quality and the precision of the subsequent processing. In this paper, a variational model is proposed by employing spectral-spatial adaptive unidirectional variation and a sparse representation. Unlike traditional methods, we exploit the spectral correction and remove stripes in different bands and different regions adaptively, instead of selecting parameters band by band. The regularization strength adapts to the spectrally varying stripe intensities and the spatially varying texture information. Spectral correlation is exploited via dictionary learning in the sparse representation framework to prevent spectral distortion. Moreover, the minimization problem, which contains two unsmooth and inseparable l1-norm terms, is optimized by the split Bregman approach. Experimental results, on datasets from several imaging systems, demonstrate that the proposed method can remove stripe noise effectively and adaptively, as well as preserve original detail information.

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Section: Resultsmentioning

confidence: 99%

“…Motivated by this observation, the authors of Ref. [10] formulated an intuitive unidirectional variation (UV) model as ( )…”

Section: Problem Formulationmentioning

confidence: 99%

See 1 more Smart Citation

Destripe Hyperspectral Images with Spectral-spatial Adaptive Unidirectional Variation and Sparse Representation

Zhou

Wang

Huo

et al. 2016

Journal of the Optical Society of Korea

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“…In remote sensing images stripe noise removal problem, the stripe effects can be regarded as additive noise [24,25,35], and the degradation model can be given by f(x, y) = u(x, y) + s(x, y) + n(x, y),…”

Section: Problem Formulation and Image Decomposition Frameworkmentioning

confidence: 99%

Stripe noise removal of remote sensing images by total variation regularization and group sparsity constraint

et al. 2017

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Remote sensing images have been used in many fields, such as urban planning, military, and environment monitoring, but corruption by stripe noise limits its subsequent applications. Most existing stripe noise removal (destriping) methods aim to directly estimate the clear images from the stripe images without considering the intrinsic properties of stripe noise, which causes the image structure destroyed. In this paper, we propose a new destriping method from the perspective of image decomposition, which takes the intrinsic properties of stripe noise and image characteristics into full consideration. The proposed method integrates the unidirectional total variation (TV) regularization, group sparsity regularization, and TV regularization together in an image decomposition framework. The first two terms are utilized to exploit the stripe noise properties by implementing statistical analysis, and the TV regularization is adopted to explore the spatial piecewise smooth structure of stripe-free image. Moreover, an efficient alternating minimization scheme is designed to solve the proposed model. Extensive experiments on simulated and real data demonstrate that our method outperforms several existing state-of-the-art destriping methods in terms of both quantitative and qualitative assessments.

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“…Several other destriping techniques have been used for MODIS and other instruments [26] and we considered these, but have found that histogram matching is the most versatile. It is also the only technique capable of handling the large dynamic range of the DNB [27].…”

mentioning

confidence: 99%

VIIRS Day/Night Band—Correcting Striping and Nonuniformity over a Very Large Dynamic Range

Mills¹,

Miller

2016

J. Imaging

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Abstract:The Suomi National Polar-orbiting (NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Day-Night Band (DNB) measures visible and near-infrared light extending over seven orders of magnitude of dynamic range. This makes radiometric calibration difficult. We have observed that DNB imagery has striping, banding and other nonuniformities-day or night. We identified the causes as stray light, nonlinearity, detector crosstalk, hysteresis and mirror-side variation. We found that these affect both Earth-view and calibration signals. These present an obstacle to interpretation by users of DNB products. Because of the nonlinearity we chose the histogram matching destriping technique which we found is successful for daytime, twilight and nighttime scenes. Because of the very large dynamic range of the DNB, we needed to add special processes to the histogram matching to destripe all scenes, especially imagery in the twilight regions where scene illumination changes rapidly over short distances. We show that destriping aids image analysts, and makes it possible for advanced automated cloud typing algorithms. Manual or automatic identification of other features, including polar ice and gravity waves in the upper atmosphere are also discussed. In consideration of the large volume of data produced 24 h a day by the VIIRS DNB, we present methods for reducing processing time.

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Toward Optimal Destriping of MODIS Data Using a Unidirectional Variational Model

Cited by 207 publications

References 21 publications

Destripe Hyperspectral Images with Spectral-spatial Adaptive Unidirectional Variation and Sparse Representation

Destripe Hyperspectral Images with Spectral-spatial Adaptive Unidirectional Variation and Sparse Representation

Stripe noise removal of remote sensing images by total variation regularization and group sparsity constraint

VIIRS Day/Night Band—Correcting Striping and Nonuniformity over a Very Large Dynamic Range

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