Satellite Imagery is one of the most widely used sources to analyze geographic features and environments in the world. The data gathered from satellites are used to quantify many vital problems facing our society, such as the impact of natural disasters, shore erosion, rising water levels, and urban growth rates. In this paper, we construct machine learning and deep learning algorithms for repairing anomalies in the Landsat satellite imagery data which arise for various reasons ranging from cloud obstruction to satellite malfunctions. The accuracy of GIS data is crucial to ensuring the models produced from such data are as close to reality as possible. Reducing the inherent bias caused by the obstruction or obfuscation of reflectance values is a simple but effective way to more closely represent the reality of our environment with satellite data. Using clean pixels from previously acquired satellite imagery, we were able to model the bias present in each scene at different times and apply algorithms to fix the inconsistencies. The machine learning model decreased the mean absolute error by an average of 80.1% compared to traditional repair algorithms such as mosaicking.
Lane, Griffin J.; Goresen, Patricia; and Slater, Robert
"Repairing Landsat Satellite Imagery Using Deep Machine Learning Techniques,"
SMU Data Science Review: Vol. 2:
1, Article 12.
Available at: https://scholar.smu.edu/datasciencereview/vol2/iss1/12
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