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A DEEP LEARNING MODEL FOR ATMOSPHERIC CORRECTION OF SATELLITE IMAGES
  • Maitrik Shah ,
  • Mehul Raval
Maitrik Shah
Ahmedabad University

Corresponding Author:[email protected]

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Mehul Raval
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Abstract

Satellite images captured by the sensor do not contain the reflectance values returned from the earth due to absorption and scattering caused by the atmospheric elements. Atmospheric correction (AC) retrieves the actual reflectance values, called surface reflectance (SR). Existing models are based on physics-based radiative transfer codes that rely on precomputed lookup tables, require many atmospheric parameters, and involve high computation costs. A few of these parameters are difficult to calculate and must be estimated. Deep learning models can be an excellent approximator to such physics-based AC models. In the proposed paper, we design, develop and analyze a deep learning end-to-end model. It is trained with a seasonally and spatially rich dataset to perform AC of Landsat 8 satellite images without explicitly considering atmospheric parameters. Experiments on model predictions are carried out to validate the effectiveness using Landsat data and ground measurements provided by RadCalNet. The results are encouraging because the model predicts SR values with good accuracy and establishes a high correlation with the reference data.