Fully Convolutional Neural Networks For Remote Sensing Image Classification

Emmanuel Maggiori 1, * Yuliya Tarabalka 1 Guillaume Charpiat 2 Pierre Alliez 1
* Corresponding author
1 TITANE - Geometric Modeling of 3D Environments
CRISAM - Inria Sophia Antipolis - Méditerranée
2 TAO - Machine Learning and Optimisation
LRI - Laboratoire de Recherche en Informatique, UP11 - Université Paris-Sud - Paris 11, Inria Saclay - Ile de France, CNRS - Centre National de la Recherche Scientifique : UMR8623
Abstract : We propose a convolutional neural network (CNN) model for remote sensing image classification. Using CNNs provides us with a means of learning contextual features for large-scale image labeling. Our network consists of four stacked convolutional layers that downsample the image and extract relevant features. On top of these, a deconvolutional layer upsamples the data back to the initial resolution, producing a final dense image labeling. Contrary to previous frameworks, our network contains only convolution and deconvolution operations. Experiments on aerial images show that our network produces more accurate classifications in lower computational time.
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Emmanuel Maggiori, Yuliya Tarabalka, Guillaume Charpiat, Pierre Alliez. Fully Convolutional Neural Networks For Remote Sensing Image Classification. IEEE International Geoscience and Remote Sensing Symposium, IEEE GRSS, Jul 2016, Beijing, China. pp.5071-5074. ⟨hal-01350706⟩

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