Learning and Adapting Robust Features for Satellite Image Segmentation on Heterogeneous Data Sets

This paper addresses the problem of training a deep neural network for satellite image segmentation so that it can be deployed over images whose statistics differ from those used for training. For example, in postdisaster damage assessment, the tight time constraints make it impractical to train a n...

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Bibliographic Details
Published in:	IEEE transactions on geoscience and remote sensing Vol. 57; no. 9; pp. 6517 - 6529
Main Authors:	Ghassemi, Sina, Fiandrotti, Attilio, Francini, Gianluca, Magli, Enrico
Format:	Journal Article
Language:	English
Published:	New York IEEE 01.09.2019 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Artificial neural networks Coders Convolutional neural network (CNN) Damage assessment Datasets deep learning domain adaptation encoder–decoder architecture Human performance Image processing Image segmentation Neural networks satellite image segmentation Satellite imagery Spaceborne remote sensing Statistical methods Statistics Target recognition Training
ISSN:	0196-2892, 1558-0644
Online Access:	Get full text
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