Striping removal in MOS-B data

Corsini, Giovanni; Diani, Marco; Walzel, Thomas

doi:10.1109/36.843038

Cited by 78 publications

(18 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Aside from the WM filter, the striping algorithms proposed by Lee [36], Gao et al [37], Chen et al [18], Crippen [38], Corsini et al [39], and Jung et al [40] can also be used in this step according to different characteristics of the residual stripe noises.…”

Section: B Image Statisticsmentioning

confidence: 99%

See 1 more Smart Citation

A Relative Radiometric Correction Method for Airborne Image Using Outdoor Calibration and Image Statistics

Duan

Chen

Yan

2014

IEEE Trans. Geosci. Remote Sensing

View full text Add to dashboard Cite

The Airborne Hyperspectral Imager (AHI) is a civilian remote sensor made in China, which uses a multichip chargecoupled device (CCD) butting technology to enlarge the frame. With the aging of the sensor, overall nonuniformity and stripe noises are observed. To solve these problems, a new idea of using the outdoor calibration and the image statistics together was developed to correct AHI images. In this method, outdoor calibration was conducted to calculate the relative radiometric calibration coefficient of each CCD detector for correcting the overall nonuniformity of an image. Then, image statistics were used to remove the residual stripes. This paper presents the principle, the experiment, and the results of this method. To validate the effectiveness of the proposed method, it was compared with some other methods and was evaluated by three quality indices. Moreover, the effects of each step of this method were analyzed separately. The experimental results suggest that the proposed method is convenient and practical for the relative radiometric correction of an airborne image.

show abstract

Section: B Image Statisticsmentioning

confidence: 99%

“…The lesser the change, the better the correction effects [18]. IF has been used in [18] and [39] to measure the corrected image. It is defined as follows:…”

Section: A Validation Of the Modelmentioning

confidence: 99%

A Relative Radiometric Correction Method for Airborne Image Using Outdoor Calibration and Image Statistics

Duan

Chen

Yan

2014

IEEE Trans. Geosci. Remote Sensing

View full text Add to dashboard Cite

show abstract

“…To some extent, the facet filter could suppress the residual noise and random noise. Radiometric equalizationbased approach was also applied to remove non-periodic stripes [18].…”

Section: B Related Destriping Algorithmsmentioning

confidence: 99%

Anisotropic Spectral-Spatial Total Variation Model for Multispectral Remote Sensing Image Destriping

Chang

Yan

Fang

et al. 2015

IEEE Trans. on Image Process.

197

View full text Add to dashboard Cite

Multispectral remote sensing images often suffer from the common problem of stripe noise, which greatly degrades the imaging quality and limits the precision of the subsequent processing. The conventional destriping approaches usually remove stripe noise band by band, and show their limitations on different types of stripe noise. In this paper, we tentatively categorize the stripes in remote sensing images in a more comprehensive manner. We propose to treat the multispectral images as a spectral-spatial volume and pose an anisotropic spectral-spatial total variation regularization to enhance the smoothness of solution along both the spectral and spatial dimension. As a result, a more comprehensive stripes and random noise are perfectly removed, while the edges and detail information are well preserved. In addition, the split Bregman iteration method is employed to solve the resulting minimization problem, which highly reduces the computational load. We extensively validate our method under various stripe categories and show comparison with other approaches with respect to result quality, running time, and quantitative assessments.

show abstract

“…Isostatic compensation method was proposed by Weinreb et al [6] in 1989, which was used into data recovery for geostationary operational environmental satellite(GOES) images. This method [7] did not need histogram matching and moment matching, and used directly equalization curves of selected training site images to match other areas. But due to the artificial selection, the results had man-made errors.…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral Remote Sensing Data Recovery via Adaptive Window Matching Method

Zeng¹,

Lan²,

Han³

et al. 2015

Int. J. Onl. Eng.

View full text Add to dashboard Cite

Abstract-HJ-1A satellite is often used to monitor environmental disaster and plays an important role in environmental changes. Because of the affection of various factors, certain band of HJ-1A hyperspectral remote sensing data is severe loss or distortion, which brings great difficulties for subsequent quantitative processing. A novel adaptive window matching algorithm, which can adjust intelligently size of matching window according to different local feature information of the image, is proposed for HJ-1A satellite hyperspectral data recovery in this paper. The results show that the adaptive matching algorithm has a more superior performance than other algorithms in image quality index, column mean curve, and image correlation coefficient.

show abstract

Striping removal in MOS-B data

Cited by 78 publications

References 3 publications

A Relative Radiometric Correction Method for Airborne Image Using Outdoor Calibration and Image Statistics

A Relative Radiometric Correction Method for Airborne Image Using Outdoor Calibration and Image Statistics

Anisotropic Spectral-Spatial Total Variation Model for Multispectral Remote Sensing Image Destriping

Hyperspectral Remote Sensing Data Recovery via Adaptive Window Matching Method

Contact Info

Product

Resources

About