TY - JOUR
T1 - Estimation of remote sensing imagery atmospheric conditions using deep learning and image classification
AU - Korzh, Oxana
AU - Serra, Edoardo
N1 - Publisher Copyright:
© Springer Nature Switzerland AG 2019.
PY - 2018
Y1 - 2018
N2 - 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.
AB - 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.
KW - Classification and regression trees
KW - Deep learning
KW - Neural networks
KW - Transfer learning
UR - https://www.scopus.com/pages/publications/85060499323
U2 - 10.1007/978-3-030-01057-7_93
DO - 10.1007/978-3-030-01057-7_93
M3 - Article
AN - SCOPUS:85060499323
SN - 2194-5357
VL - 869
SP - 1237
EP - 1244
JO - Advances in Intelligent Systems and Computing
JF - Advances in Intelligent Systems and Computing
ER -
