Global heterogeneous graph convolutional network: from coarse to refined land cover and land use segmentation-Reference-Cited by-同舟云学术

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Global heterogeneous graph convolutional network: from coarse to refined land cover and land use segmentation

Published:2024-05-14 Issue:1 Volume:17 Page:
ISSN:1753-8947
Container-title:International Journal of Digital Earth
language:en
Short-container-title:International Journal of Digital Earth

Author:

Liu Zhi-Qiang¹²^ORCID,Zhang Zheng¹^ORCID,Meng Yu¹^ORCID,Tang Ping¹

Affiliation:

1. Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, People’s Republic of China

2. School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing, People’s Republic of China

Funder

National Key R&D Program of China

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/17538947.2024.2353110

Reference71 articles.

1. SLIC Superpixels Compared to State-of-the-Art Superpixel Methods

2. AN ITERATIVE INFERENCE PROCEDURE APPLYING CONDITIONAL RANDOM FIELDS FOR SIMULTANEOUS CLASSIFICATION OF LAND COVER AND LAND USE

3. Inferring Urban Land Use from Satellite Sensor Images Using Kernel-Based Spatial Reclassification;Barnsley Michael J.;Photogrammetric Engineering and Remote Sensing,1996

4. Distinguishing urban land-use categories in fine spatial resolution land-cover data using a graph-based, structural pattern recognition system

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