Rethinking BiSeNet: A Lightweight Network for Urban Water Extraction
Author:
Affiliation:
1. College of Geophysics, Chengdu University of Technology, Chengdu, China
Funder
Key Research and Development Program of Sichuan Province China
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
General Earth and Planetary Sciences,Electrical and Electronic Engineering
Link
http://xplorestaging.ieee.org/ielx7/36/10006360/10098639.pdf?arnumber=10098639
Reference35 articles.
1. Classification for high resolution remote sensing imagery using a fully convolutional network;fu;Remote Sens,2017
2. An Urban Water Extraction Method Combining Deep Learning and Google Earth Engine
3. Extraction of Urban Water Bodies from High-Resolution Remote-Sensing Imagery Using Deep Learning
4. Water Body Extraction from Very High Spatial Resolution Remote Sensing Data Based on Fully Convolutional Networks
5. Road Extraction from High-Resolution Remote Sensing Imagery Using Deep Learning
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