A novel scene-based non-uniformity correction method for SWIR push-broom hyperspectral sensors

Hu, Binlin; Hao, Shijing; Sun, Dexin; Liu, Yinnian

doi:10.1016/j.isprsjprs.2017.08.004

Cited by 11 publications

(8 citation statements)

References 22 publications

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“…The documentation also notes that these artefacts are minimized after relative radiometric correction, which is included in level 1B data. Non-uniformity has been reported in other satellite sensors including in imagery from NASA/USGSʻs Landsat 8 ( Pahlevan et al 2017b ), ESAʻs Sentinel-2 mission ( Pahlevan et al 2017a ), Maxarʻs WorldView-1 satellite ( Krause 2008 ), Planet Labʻs RapidEye satellite constellation ( Anderson et al 2011 ), and more generally for short-wavelength infrared pushbroom hyperspectral sensors ( Hu et al 2017 ).…”

Section: Resultsmentioning

confidence: 99%

Vertical artifacts in high-resolution WorldView-2 and WorldView-3 satellite imagery of aquatic systems

Coffer

Whitman

Schaeffer

et al. 2022

International Journal of Remote Sensing

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Satellite image artefacts are features that appear in an image but not in the original imaged object and can negatively impact the interpretation of satellite data. Vertical artefacts are linear features oriented in the along-track direction of an image system and can present as either banding or striping; banding are features with a consistent width, and striping are features with inconsistent widths. This study used high-resolution data from DigitalGlobeʻs (now Maxar) WorldView-3 satellite collected at Lake Okeechobee, Florida (FL), on 30 August 2017. This study investigated the impact of vertical artefacts on both at-sensor radiance and a spectral index for an aquatic target as WorldView-3 was primarily designed as a land sensor. At-sensor radiance measured by six of WorldView-3ʻs eight spectral bands exhibited banding, more specifically referred to as non-uniformity, at a width corresponding to the multispectral detector sub-arrays that comprise the WorldView-3 focal plane. At-sensor radiance measured by the remaining two spectral bands, red and near-infrared (NIR) #1, exhibited striping. Striping in these spectral bands can be attributed to their time delay integration (TDI) settings at the time of image acquisition, which were optimized for land. The impact of vertical striping on a spectral index leveraging the red, red edge, and NIR spectral bands—referred to here as the NIR maximum chlorophyll index (MCI NIR )—was investigated. Temporally similar imagery from the European Space Agencyʻs Sentinel-3 and Sentinel-2 satellites were used as baseline references of expected chlorophyll values across Lake Okeechobee as neither Sentinel-3 nor Sentinel-2 imagery showed striping. Striping was highly prominent in the MCI NIR product generated using WorldView-3 imagery, as noise in the at-sensor radiance exceeded any signal of chlorophyll in the image. Adjusting the image acquisition parameters for future tasking of WorldView-3 or the functionally similar WorldView-2 satellite may alleviate these artefacts. To test this, an additional WorldView-3 image was acquired at Lake Okeechobee, FL, on 26 May 2021 in which the TDI settings and scan line rate were adjusted to improve the signal-to-noise ratio. While some evidence of non-uniformity remained, striping was no longer noticeable in the MCI NIR product. Future image tasking over aquatic targets should employ these updated image acquisition parameters. Since the red and NIR #1 spectral bands are critical for inland and coastal water applications, archived images not collected using these updated settings may be limited in their potential for analysis of aquatic variables that require these two spectral bands to derive

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Section: Resultsmentioning

confidence: 99%

Vertical artifacts in high-resolution WorldView-2 and WorldView-3 satellite imagery of aquatic systems

Coffer

Whitman

Schaeffer

et al. 2022

International Journal of Remote Sensing

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“…However, most denoising algorithms aim to remove AWGN, one of the noise types, and show poor performance when applied to the complex and various types of noise generated in software and hardware for a wide range of multimedia applications. Research is also being conducted using a scene-based non-uniform correction (SBNUC) method [1] to remove line noise that is often observed in HS systems. Also, several CNN-based methods have been studied to solve these problems [14], [15], [16].…”

Section: Related Workmentioning

confidence: 99%

“…As examples of these types, Fig. 2 shows image degradation due to complex noise in a thermal infrared (TIR) image or a 1D scan-based hyper-spectral (HS) image of a special design structure [1]. Image deterioration due to image degradation and noise can be inconvenient for users and causes the loss of important image information.…”

Section: Introductionmentioning

confidence: 99%

“…Recent advances in convolutional neural networks (CNNs), software, and hardware have shown that deep learning [6]based algorithms (VDSR [7], DnCNN [8], FFDNet [9], MWCNN [10], Noise2Noise [11]) rank high in state-of-theart (SOTA)-based paperwithcode 1 . However, side effects of these techniques include image distortion, and limitations are found in the removal of complex noise components, as well as in overall speed.…”

Section: Introductionmentioning

confidence: 99%

“…However, side effects of these techniques include image distortion, and limitations are found in the removal of complex noise components, as well as in overall speed. In the present paper, we propose the 1 [Online]. Available: http://paperswithcode.com/sota performance of image improvement by constructing a network based on global residual learning (GRL) and local residual learning (LRL).…”

Section: Introductionmentioning

confidence: 99%

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Residual Forward-Subtracted U-Shaped Network for Dynamic and Static Image Restoration

Jung

Kim

2020

IEEE Access

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Estimation of shallow stream bathymetry under varying suspended sediment concentrations and compositions using hyperspectral imagery

et al. 2023

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A novel scene-based non-uniformity correction method for SWIR push-broom hyperspectral sensors

Cited by 11 publications

References 22 publications

Vertical artifacts in high-resolution WorldView-2 and WorldView-3 satellite imagery of aquatic systems

Vertical artifacts in high-resolution WorldView-2 and WorldView-3 satellite imagery of aquatic systems

Residual Forward-Subtracted U-Shaped Network for Dynamic and Static Image Restoration

Estimation of shallow stream bathymetry under varying suspended sediment concentrations and compositions using hyperspectral imagery

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