Destriping LANDSAT MSS images by histogram modification

Horn, Berthold K. P.; Woodham, Robert J.

doi:10.1016/0146-664x(79)90035-2

Cited by 198 publications

(78 citation statements)

References 6 publications

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“…Spatial domain image convolution can be applied to rectify affected MODIS images, but important data can be lost due to image blurring. Users of other data products prone to striping -such as Landsat MSS (Multi-Spectral Scanner) images -have used techniques such as histogram matching, which are broadly applicable to MODIS imagery (Horn and Woodham, 1979). Several papers (Antonelli et al, 2004;di Bisceglie et al, 2009) have reported the use of the 'bow-tie effect' inherent to MODIS level 1b data as a form of data redundancy, facilitating the correction of data from the poorly calibrated detectors.…”

Section: Maximum Value Compositing (Mvc) Is a Class Of Compositing Almentioning

confidence: 99%

East Antarctic Landfast Sea Ice Distribution and Variability, 2000–08

et al. 2012

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This study presents the first continuous, high spatiotemporal resolution time series of landfast sea ice extent along the East Antarctic coast for the period March 2000–December 2008. The time series was derived from consecutive 20-day cloud-free Moderate Resolution Imaging Spectroradiometer (MODIS) composite images. Fast ice extent across the East Antarctic coast shows a statistically significant (1.43% ±0.30% yr−1) increase. Regionally, there is a strong increase in the Indian Ocean sector (20°–90°E, 4.07% ±0.42% yr−1), and a nonsignificant decrease in the western Pacific Ocean sector (90°–160°E, −0.40% ±0.37% yr−1). An apparent shift from a negative to a positive extent trend is observed in the Indian Ocean sector from 2004. This shift also coincides with a greater amount of interannual variability. No such shift in apparent trend is observed in the western Pacific Ocean sector, where fast ice extent is typically higher and variability lower than the Indian Ocean sector. The limit to the maximum fast ice areal extent imposed by the location of grounded icebergs modulates the shape of the mean annual fast ice extent cycle to give a broad maximum and an abrupt, relatively transient minimum. Ten distinct fast ice regimes are identified, related to variations in bathymetry and coastal configuration. Fast ice is observed to form in bays, on the windward side of large grounded icebergs, between groups of smaller grounded icebergs, between promontories, and upwind of coastal features (e.g., glacier tongues). Analysis of the timing of fast ice maxima and minima is also presented and compared with overall sea ice maxima/minima timing.

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Section: Maximum Value Compositing (Mvc) Is a Class Of Compositing Almentioning

confidence: 99%

East Antarctic Landfast Sea Ice Distribution and Variability, 2000–08

et al. 2012

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“…Then for each grey-level g 1 the grey-level g 2 is calculated for which f 1 (g 1 ) = f 2 (g 2 ) as shown in Fig. 2, and this is the result of histogram matching function M(g 1 ) = g 2 (Horn & Woodham, 1979).…”

Section: Histogram Specification (Matching)mentioning

confidence: 99%

Histogram Equalization Implementation in the Preprocessing Phase on Optical Character Recognition

Pangestu¹,

Gunawan²,

Hansun³

2017

IJTech

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“…HMM was originally designed for Landsat [28], and equalizes cumulative histograms of radiance distributions per detector in order to correct offset, gain and nonlinearity. We believe that HMM is better than MM because it is able to correct nonlinearity, whereas MM cannot.…”

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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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Destriping LANDSAT MSS images by histogram modification

Cited by 198 publications

References 6 publications

East Antarctic Landfast Sea Ice Distribution and Variability, 2000–08

East Antarctic Landfast Sea Ice Distribution and Variability, 2000–08

Histogram Equalization Implementation in the Preprocessing Phase on Optical Character Recognition

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

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