EDG-Net: Edge-Enhanced Dynamic Graph Convolutional Network for Remote Sensing Scene Classification of Mining-Disturbed Land

EDG-Net: Edge-Enhanced Dynamic Graph Convolutional Network for Remote Sensing Scene Classification of Mining-Disturbed Land

Scene classification and mapping of surface mining-disturbed land can attain semantic-level information that is useful for monitoring mine geo-environment. Mining land’s complex characteristics makes it difficult to extract key features, restricting the accuracy improvements. This study f...

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Bibliographic Details
Main Authors: Xianju Li, Pan Kong, Weitao Chen, Wenxi He, Jian Feng, Jiangyuan Wang
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Subjects:
Graph convolution network (GCN)
mining land
multiscale attention
remote sensing
scene classification
Online Access:https://ieeexplore.ieee.org/document/11063351/
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https://ieeexplore.ieee.org/document/11063351/

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