Abstract
Deep Learning, and most notable Deep Neural Networks, have largely driven Artificial Intelligence in the area of remote sensing, mainly image classification tasks. In this paper, we present an approach based on Convolutional Neural Networks to classify Earth Observation satellite images as environmental preserved or non-preserved areas. One interesting feature of our approach is the fact that we used sensors with different spatial resolutions to assess the performance of a traditional network. We relied on images from the Tocantins Cerrado obtained by the Wide-Scan Multispectral and Panchromatic Camera of the CBERS-4A satellite, with a spatial resolution of 8m to create the training dataset. For testing, we set up a set of images of the Sentinel satellite, with a spatial resolution of 10m from Goiás Cerrado. Results imply that Convolutional Neural Networks are feasible and are a good alternative for classifying remote sensing areas even when dealing with images from various sensors, and also with different spatial resolutions, where the model used in this study obtained an accuracy of 0.87. This study demonstrates the flexibility of Convolutional Neural Networks concerning the ability to generalize knowledge for classifying remote sensing images.
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Miranda, M.d.S., de Santiago, V.A., Körting, T.S., Leonardi, R., de Freitas, M.L. (2021). Deep Convolutional Neural Network for Classifying Satellite Images with Heterogeneous Spatial Resolutions. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2021. ICCSA 2021. Lecture Notes in Computer Science(), vol 12955. Springer, Cham. https://doi.org/10.1007/978-3-030-87007-2_37
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