An approach to fill in missing data from satellite imagery using data-intensive computing and DINEOF.

This paper proposes an approach to fill in missing data from satellite images using data-intensive computing platforms. The proposed approach merges satellite imagery from diverse sources to reduce the impact of the holes in images that result from acquisition conditions: occlusion, the satellite trajectory, sunlight, among others. The amount of computation effort derived from the use of large high-resolution images is addressed by data-intensive computing techniques that assume an underlying cluster architecture. As a start, satellite data from the region of study are automatically downloaded; then, data from different sensors are corrected and merged to obtain an orthomosaic; finally, the orthomosaic is split into user-defined segments to fill in missing data, and filled segments are assembled to produce an orthomosaic with a reduced amount of missing data. As a proof of concept, the proposed data-intensive approach was implemented to study the concentration of chlorophyll at the Mexican oceans by merging data from MODIS-TERRA, MODIS-AQUA, VIIRS-SNPP, and VIIRS-JPSS-1 sensors. The results revealed that the proposed approach produces results that are similar to state-of-the-art approaches to estimate chlorophyll concentration but avoid memory overflow with large images. Visual and statistical comparison of the resulting images revealed that the proposed approach provides a more accurate estimation of chlorophyll concentration when compared to the mean of pixels method alone.