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Deep Learning Classification of Land Cover and Crop Types Using Remote Sensing Data

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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Additional Info

Field Value
Author(s) Nataliia Kussul, Mykola Lavreniuk, Sergii Skakun, and Andrii Shelestov
Last Updated February 11, 2021, 20:00 (UTC)
Created December 8, 2020, 04:57 (UTC)
Stable Link
Date 2017-05-01
Publishing Body IEEE Geoscience and Remote Sensing Letters
Content Type Publications
Primary Category Land Use & Land Cover
Sub Category Automatic Crop Mapping/AG
Country Name Ukraine
Location-Region/City Kyiv
Publishing Organization New Light Technologies