IEEE Publication Encouragement Award 2017
Paper "Deep Learning Classification of Land Cover and Crop Types Using Remote Sensing Data" in IEEE Geoscience and Remote Sensing Letters by Prof. Nataliia Kussul, Mykola Lavreniuk, PhD Sergii Skakun and Prof. Andrii Shelestov
has been recognized Üó IEEE Publication Encouragement Award 2017.
This tradition of awarding an encouragement prize to Ukrainian IEEE members for publication in IEEE Joumals related to AP/MTT/ED/AES/GRS/NPS technical societies carries on for 15 years.
This year, there were 15 Ukrainian teams of contributors whose papers have been puÜlished this year in IEEE Magazines.
Deep learning (DL) is a powerful state-of-the-art technique for image processing including remote sensing (RS) images. This letter describes a multilevel DL architecture that targets land
cover and crop type classification from multitemporal multisource satellite imagery. The pillars of the architecture are unsupervised neural network (NN) that is used for optical
imagery segmentation and missing data restoration due to clouds and shadows, and an ensemble of supervised NNs. As basic supervised NN architecture, we use a traditional fully connected
multilayer perceptron (MLP) and the most commonly used approach in RS community random forest, and compare them with convolutional NNs (CNNs). Experiments are carried out for the joint experiment
of crop assessment and monitoring test site in Ukraine for classification of crops in a heterogeneous environment using nineteen multitemporal scenes acquired by Landsat-8 and Sentinel-1A RS satellites.
The architecture with an ensemble of CNNs outperforms the one with MLPs allowing us to better discriminate certain summer crop types, in particular maize and soybeans, and yielding the target accuracies
more than 85% for all major crops (wheat, maize, sunflower, soybeans, and sugar beet).
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