An automated image destriping for improving data quality of Oceansat2 OCM — Oceanic geophysical products

Ocean Colour Monitor (OCM) in Oceansat-2 mission is a push-broom sensor with a spatial resolution of 360m, containing eight spectral channels operating in visible-Near IR spectral range with a swath of 1420 km across scan. The residual detector-to-detector variations appear as a along track striping in derived products, even though the rigorous effort has been put to minimize them during radiometric normalization. Oceanic geophysical products (chlorophyll-a, total sediment matter, yellow substance etc.) suffer from severe vertical striping, by virtue of very low signal return from ocean water and its constituents, thus obscuring the visual interpretation of these products. An algorithm is developed to destrip the original products, based on Lyon´s algorithm with some modifications. The modification includes a) reduction of window size averaging, b) accounting for edge pixels outside the window size region and C) mean-value normalization to bring radiometry of pre- and post-correction. This automated algorithm works on scene-based image analysis to estimate the compensation factor for each pixel. It has been found that the proposed algorithm brings down the striping significantly between the original and the corrected geophysical product. This paper outlines modified Lyon´s de-striping algorithm and quantitative analysis of the effectiveness of the algorithm on the final products.