Atmospheric correction of multispectral satellite imagery

• Дата: 2020
• Автор: Katkovsky, Leonid
• Формат: Стаття
• Мова: rus
• Опубліковано: Scientific Centre for Aerospace Research of the Earth Institute of Geological Science National Academy of Sciences of Ukraine, Kyiv, Ukraine 2020
• Теми: satellite imagery; broadband sensor; multispectral image; atmospheric model; analytical formulae; reflectance; regression equations; спутниковая съемка; “широкие” спектральные каналы; многоспектральное изображение; модель атмосферы; аналитические соотношения; альбедо; регрессионные уравнения; супутникова зйомка; “широкі” спектральні канали; багатоспектральне зображення; модель атмосфери; аналітичні співвідношення; регресивні рівняння
• Онлайн доступ: https://ujrs.org.ua/ujrs/article/view/170
• Назва журналу: Ukrainian Journal of Remote Sensing of the Earth

Atmospheric correction is a necessary step in the processing of remote sensing data acquired in the visible and NIR spectral bands.The paper describes the developed atmospheric correction technique for multispectral satellite data with a small number of relatively broad spectral bands (not hyperspectral). The technique is based on the proposed analytical formulae that expressed the spectrum of outgoing radiation at the top of a cloudless atmosphere with rather high accuracy. The technique uses a model of the atmosphere and its optical and physical parameters that are significant from the point of view of radiation transfer, the atmosphere is considered homogeneous within a satellite image. To solve the system of equations containing the measured radiance of the outgoing radiation in the bands of the satellite sensor, the number of which is less than the number of unknowns of the model, it is proposed to use various additional relations, including regression relations between the optical parameters of the atmosphere. For a particular image pixel selected in a special way, unknown atmospheric parameters are found, which are then used to calculate the reflectance for all other pixels.Testing the proposed technique on OLI sensor data of Landsat 8 satellite showed higher accuracy in comparison with the FLAASH and QUAC methods implemented in the well-known ENVI image processing software. The technique is fast and there is using no additional information about the atmosphere or land surface except images under correction.