Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/30374
Appears in Collections:Computing Science and Mathematics Journal Articles
Peer Review Status: Refereed
Title: Dynamic Multicontext Segmentation of Remote Sensing Images Based on Convolutional Networks
Author(s): Nogueira, Keiller
Dalla Mura, Mauro
Chanussot, Jocelyn
Schwartz, William Robson
dos Santos, Jefersson Alex
Contact Email: keiller.nogueira@stir.ac.uk
Keywords: Convolutional networks (ConvNets)
deep learning
multicontext
multiscale
remote sensing
semantic segmentation
Issue Date: Oct-2019
Citation: Nogueira K, Dalla Mura M, Chanussot J, Schwartz WR & dos Santos JA (2019) Dynamic Multicontext Segmentation of Remote Sensing Images Based on Convolutional Networks. IEEE Transactions on Geoscience and Remote Sensing, 57 (10), pp. 7503-7520. https://doi.org/10.1109/tgrs.2019.2913861
Abstract: Semantic segmentation requires methods capable of learning high-level features while dealing with large volume of data. Toward such goal, convolutional networks can learn specific and adaptable features based on the data. However, these networks are not capable of processing a whole remote sensing image, given its huge size. To overcome such limitation, the image is processed using fixed size patches. The definition of the input patch size is usually performed empirically (evaluating several sizes) or imposed (by network constraint). Both strategies suffer from drawbacks and could not lead to the best patch size. To alleviate this problem, several works exploited multicontext information by combining networks or layers. This process increases the number of parameters, resulting in a more difficult model to train. In this paper, we propose a novel technique to perform semantic segmentation of remote sensing images that exploits a multicontext paradigm without increasing the number of parameters while defining, in training time, the best patch size. The main idea is to train a dilated network with distinct patch sizes, allowing it to capture multicontext characteristics from heterogeneous contexts. While processing these varying patches, the network provides a score for each patch size, helping in the definition of the best size for the current scenario. A systematic evaluation of the proposed algorithm is conducted using four high-resolution remote sensing data sets with very distinct properties. Our results show that the proposed algorithm provides improvements in pixelwise classification accuracy when compared to the state-of-the-art methods.
DOI Link: 10.1109/tgrs.2019.2913861
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