Recovering from Catastrophic Receptive Field Overflow in Semantic Segmentation of High Resolution Images: Application to Seabed Characterization
Author:
Affiliation:
1. Royal Military Academy,Dept. of Communications, Information, Systems and Sensors,Brussels
2. Ghent University,Dept. of Telecommunications and Information Processing,Ghent
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx8/10640349/10640352/10642639.pdf?arnumber=10642639
Reference10 articles.
1. Land-Use Mapping for High-Spatial Resolution Remote Sensing Image Via Deep Learning: A Review
2. Fully convolutional networks for semantic segmentation
3. Incorporating DeepLabv3+ and object-based image analysis for semantic segmentation of very high resolution remote sensing images
4. D4SC: Deep Supervised Semantic Segmentation for Seabed Characterisation in Low-Label Regime
5. DeepGlobe 2018: A Challenge to Parse the Earth through Satellite Images
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