Simultaneous Update of High-Resolution Land-Cover Mapping Attempt: Wuhan and the Surrounding Satellite Cities Cartography Using L2HNet
Author:
Affiliation:
1. Electronic Information School, Wuhan University, Wuhan, China
2. State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, China
Funder
National Natural Science Foundation of China
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Atmospheric Science,Computers in Earth Sciences
Link
http://xplorestaging.ieee.org/ielx7/4609443/9973430/10040739.pdf?arnumber=10040739
Reference38 articles.
1. Urban land-use mapping using a deep convolutional neural network with high spatial resolution multispectral remote sensing imagery
2. Efficient Patch-Wise Semantic Segmentation for Large-Scale Remote Sensing Images
3. Object-Scale Adaptive Convolutional Neural Networks for High-Spatial Resolution Remote Sensing Image Classification
4. Breaking the resolution barrier: A low-to-high network for large-scale high-resolution land-cover mapping using low-resolution labels
5. Towards better exploiting convolutional neural networks for remote sensing scene classification
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1. BiLSTM Classifier Model for Land Cover Change Detection and Classification;Revista de Gestão Social e Ambiental;2024-03-18
2. High-Resolution Land-Cover Mapping Based on a Cross-Resolution Deep Learning Framework and Available Low-Resolution Labels;IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing;2024
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