Authors
Sina Ghassemi, Attilio Fiandrotti, Gianluca Francini, Enrico Magli
Publication date
2019/4/17
Journal
IEEE Transactions on Geoscience and Remote Sensing
Volume
57
Issue
9
Pages
6517-6529
Publisher
IEEE
Description
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 network from scratch for each image to be segmented. We propose a convolutional encoder-decoder network able to learn visual representations of increasing semantic level as its depth increases, allowing it to generalize over a wider range of satellite images. Then, we propose two additional methods to improve the network performance over each specific image to be segmented. First, we observe that updating the batch normalization layers' statistics over the target image improves the network performance without human intervention. Second, we show that refining a trained network over a few samples of the image …
Total citations
201920202021202220232101085
Scholar articles
S Ghassemi, A Fiandrotti, G Francini, E Magli - IEEE Transactions on Geoscience and Remote …, 2019