A novel method for destriping of OCM-2 data and radiometric performance analysis for improved ocean color data products

Singh, Rajendra; Shanmugam, Palanisamy

doi:10.1016/j.asr.2018.03.021

Cited by 2 publications

(1 citation statement)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In addition to VIIRS DNB, the reflective and thermal bands of VIIRS are also affected by striping noise [18], [19]. Notable destriping techniques applied to various daytime satellite data include histogram matching [20], [21], frequency domain filtering [22], moment matching [23], Unidirectional Variational Model [24], wavelet transformation [25], Inverted Gaussian Averaging Window smoothening method [26], neural network [27] and sparse matrices approximation [28].…”

Section: Introductionmentioning

confidence: 99%

An Improved Method for Destriping of VIIRS Day/Night Band Images

Banerjee

Shanmugam

2022

IEEE Access

Self Cite

View full text Add to dashboard Cite

The VIIRS Day/Night Band (DNB) images have opened a new era of nocturnal monitoring of global ocean, lands, and atmospheric activities at a reasonably high spatial resolution of 742 m. However, the quality of VIIRS day/night band images is seriously affected with periodic horizontal stripe noises. The image quality is further reduced on the along-track scene edges due to the complex banding effects. The noise pattern and amplitude vary according to the observation time, background light condition, and the pixel position/characteristics. To enhance the utility of VIIRS day/night band image data, we developed a novel method to reduce the noise effects and evaluate the denoised output data to ensure its radiometric integrity and quality. The noise pattern is multiplicative in nature and generated due to the detector-to-detector response variation depending on the incident light condition across the scene. Accordingly, the observed pixels in the day and night images were separated into several classes using the Otsu's threshold value. The images recorded during twilight were classified using the solar zenith angles. Pixel-by-pixel correction of the noise effects was done by multiplying the row-wise detector gains and column-wise error factors for each class. This method was tested on several day/night and twilight images captured over the different regions with varying light conditions. The results are excellent in terms of successfully removing the striping noise and reconstructing the denoised images without spatial discontinuity across the scene regardless of its observation condition.

show abstract