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How Useful is Region-based Classification of Remote Sensing Images in a Deep Learning Framework?

Nicolas Audebert 1, 2 Bertrand Le Saux 2 Sébastien Lefèvre 1
1 OBELIX - Environment observation with complex imagery
UBS - Université de Bretagne Sud, IRISA-D5 - SIGNAUX ET IMAGES NUMÉRIQUES, ROBOTIQUE
Abstract : In this paper, we investigate the impact of segmentation algorithms as a preprocessing step for classification of remote sensing images in a deep learning framework. Especially, we address the issue of segmenting the image into regions to be classified using pre-trained deep neural networks as feature extractors for an SVM-based classifier. An efficient segmentation as a preprocessing step helps learning by adding a spatially-coherent structure to the data. Therefore, we compare algorithms producing superpixels with more traditional remote sensing segmentation algorithms and measure the variation in terms of classification accuracy. We establish that superpixel algorithms allow for a better classification accuracy as a homogenous and compact segmentation favors better generalization of the training samples.
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Submitted on : Wednesday, November 2, 2016 - 3:51:01 PM
Last modification on : Friday, July 10, 2020 - 4:26:01 PM
Long-term archiving on: : Friday, February 3, 2017 - 11:24:37 AM

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Nicolas Audebert, Bertrand Le Saux, Sébastien Lefèvre. How Useful is Region-based Classification of Remote Sensing Images in a Deep Learning Framework?. IEEE International Geosciences and Remote Sensing Symposium (IGARSS), Jul 2016, Beijing, China. ⟨10.1109/IGARSS.2016.7730327⟩. ⟨hal-01320016⟩

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