Abstract
Estimation of atmospheric conditions is an important problem for remote sensing imagery analysis and processing. Especially it is useful to have a fast and accurate method when collecting weekly or daily imagery of the entire land surface of the earth with high resolution. This task appears in many remote sensing applications such as tracking changes of the landscape, agricultural image analysis, landscape anomaly detection. In this paper, we propose a method of atmospheric conditions estimation based on RGB image classification using fine-tunned CNN ensemble and image classifiers. We investigate usage of CNNs (Alexnet and a pretrained CNN ensemble) as feature extractors in combination with different classifiers such as XGBoost and ExtraTrees. We have tested the proposed method on a data set provided in the kaggle contest “Planet: Understanding the Amazon from Space” where the application task is to analyze deforestation in the Amazon Basin.
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Acknowledgment
This work was partially financed by Idaho Global Entrepreneurial Mission (IGEM) program (Grant 131G106011 Precision Ag - Increasing Crop).
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Korzh, O., Serra, E. (2019). Estimation of Remote Sensing Imagery Atmospheric Conditions Using Deep Learning and Image Classification. In: Arai, K., Kapoor, S., Bhatia, R. (eds) Intelligent Systems and Applications. IntelliSys 2018. Advances in Intelligent Systems and Computing, vol 869. Springer, Cham. https://doi.org/10.1007/978-3-030-01057-7_93
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DOI: https://doi.org/10.1007/978-3-030-01057-7_93
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