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Proceedings ArticleDOI

Destriping Ocean Color Monitor-2 data

02 May 2016-Vol. 9881, pp 988123
Abstract: Ocean Color Monitor-2 (OCM-2) on-board Oceansat 2 satellite is a multi-spectral sensor with a spatial resolution of 360×250 m . Despite the presence of improved spatial resolution for better ocean color interpretation within coastal zones; differences among the OCM-2 detectors lead to striping artifacts in the along-track direction limiting the ocean color observations. Existing calibration methods do not characterize the striping noise efficiently. Destriping algorithms are generally applied to Level 2 radiance or biogeochemical products ( i.e. , post-radiometric and atmospheric correction), to remove the striping artifacts in order to ensure quality products. The present study focuses to reveal a robust method which effectively removes the striping effects in the TOA radiance products. Preliminary results obtained from this approach have been highlighted which show significant improvement in image quality for Level 1B (TOA radiance) and Level 2 (Water leaving radiance ( L w ) and biogeochemical) products. The proposed method operates on a pixel by pixel basis with an aim to maintain the spatial and spectral resolution of data and ensure image quality in the derived products.
Topics: Radiance (56%), Ocean color (53%), Image resolution (51%), Atmospheric correction (51%), Pixel (50%)
Citations
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Journal ArticleDOI
Abstract: Despite the capability of Ocean Color Monitor aboard Oceansat-2 satellite to provide frequent, high-spatial resolution, visible and near-infrared images for scientific research on coastal zones and climate data records over the global ocean, the generation of science quality ocean color products from OCM-2 data has been hampered by serious vertical striping artifacts and poor calibration of detectors. These along-track stripes are the results of variations in the relative response of the individual detectors of the OCM-2 CCD array. The random unsystematic stripes and bandings on the scene edges affect both visual interpretation and radiometric integrity of remotely sensed data, contribute to confusion in the aerosol correction process, and multiply and propagate into higher level ocean color products generated by atmospheric correction and bio-optical algorithms. Despite a number of destriping algorithms reported in the literature, complete removal of stripes without residual effects and signal distortion in both low- and high-level products is still challenging. Here, a new operational algorithm has been developed that employs an inverted gaussian function to estimate error fraction parameters, which are uncorrelated and vary in spatial, spectral and temporal domains. The algorithm is tested on a large number of OCM-2 scenes from Arabian Sea and Bay of Bengal waters contaminated with severe stripes. The destriping effectiveness of this approach is then evaluated by means of various qualitative and quantitative analyses, and by comparison with the results of the previously reported method. Clearly, the present method is more effective in terms of removing the stripe noise while preserving the radiometric integrity of the destriped OCM-2 data. Furthermore, a preliminary time-dependent calibration of the OCM-2 sensor is performed with several match-up in-situ data to evaluate its radiometric performance for ocean color applications. OCM-2 derived water-leaving radiance products obtained after calibration show a good consistency with in-situ and MODIS-Aqua observations, with errors less than the validated uncertainties of ±5% and ±35% endorsed for the remote-sensing measurements of water-leaving radiance and retrieval of chlorophyll concentrations respectively. The calibration results show a declining trend in detector sensitivity of the OCM-2 sensor, with a maximum effect in the shortwave spectrum, which provides evidence of sensor degradation and its profound effect on the striping artifacts in the OCM-2 data products.

1 citations


References
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Journal ArticleDOI
Abstract: A multiple scattering algorithm for atmospheric correction of satellite ocean colour observations is described. This algorithm, precisely designed for the MERIS instrument, globally assesses the combined contributions of aerosols and molecules to the multiple scattering regime. The approach was introduced in a previous work, where it was shown that, for a given aerosol, multiple scattering effects can be assessed through the relationship between the aerosol optical thickness and the relative increase in the path radiance that results from the progressive introduction of this aerosol within an aerosol-free atmosphere. Based on considerations about the accuracy to which the water-leaving radiances should be retrieved, the need to account for multiple scattering is argued. The principle of the algorithm is then presented, and tests and sensitivity studies (especially as regards aerosol type and vertical distribution) are performed to assess its performance in terms of errors on the retrieved water-leaving re...

215 citations


Journal ArticleDOI
TL;DR: An alternative algorithm is suggested which matches the gain and offset of each sensor to typical values, and which is resistant to the effects of outliers.
Abstract: Image destriping is necessary due to sensor-to-sensor variation within instruments. This has most often been done by assuming that each sensor views a statistically similar subimage, and a histogram of each sensor's response is made to match the overall histogram. Histogram matching shows sensitivity to violations of the similarity assumption. An alternative algorithm is suggested which matches the gain and offset of each sensor to typical values, and which is resistant to the effects of outliers. Tests on a sample image show the moment matching algorithm reduces the variance between sensors to a greater degree than histogram matching.

207 citations


Journal ArticleDOI
TL;DR: Methods are presented for obtaining the required information directly from the statistics of the sensor outputs for destriping of LANDSAT Multispectral Scanner images, applying to images obtained with any multisensor line-scan camera.
Abstract: Before images obtained by multisensor cameras can be used in image analysis, corrections must be introduced for the differences in the gain or transfer functions of the sensors. Methods are here presented for obtaining the required information directly from the statistics of the sensor outputs. The assumption is made that the probability distribution of scene radiances seen by each sensor is the same. Successful destriping of LANDSAT Multispectral Scanner (MSS) images is demonstrated. The technique applies to images obtained with any multisensor line-scan camera, however.

198 citations


Journal ArticleDOI
TL;DR: Basic statistical assumptions used in previous techniques are replaced by a much realistic geometrical consideration on the striping unidirectional variations and the resulting algorithm is tested on Aqua and Terra MODIS data contaminated with severe stripes and is shown to provide optimal qualitative and quantitative results.
Abstract: Images acquired by the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard Terra and Aqua exhibit strong detector striping. This artifact is common to most pushbroom scanners and affects both visual interpretation and radiometric integrity of remotely sensed data. A considerable effort has been made to remove stripe noise and reduce its impact on high-level products. Despite the variety of destriping algorithms proposed in the literature, complete removal of stripes without signal distortion is yet to be overcome. In this paper, we tackle the striping issue from a variational angle. Basic statistical assumptions used in previous techniques are replaced by a much realistic geometrical consideration on the striping unidirectional variations. The resulting algorithm is tested on Aqua and Terra MODIS data contaminated with severe stripes and is shown to provide optimal qualitative and quantitative results.

138 citations


Journal ArticleDOI
Abstract: Sensor stripes evident in multisensor imagery can cause subsequent image classification to fail, if not removed properly. Of the destriping algorithms investigated, the one published by Horn and Wo...

119 citations