Assessing CNN and Semantic Segmentation Models for Coarse Resolution Satellite Image Classification in Subcontinental Scale Land Cover Mapping

Assessing CNN and Semantic Segmentation Models for Coarse Resolution Satellite Image Classification in Subcontinental Scale Land Cover Mapping

Based on studies using high-medium resolution images, convolutional neural networks (CNNs) and semantic segmentation have shown superiority over classical machine learning (ML), particularly in small-scale mapping. However, few/no studies have assessed the techniques on coarse resolution...

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Bibliographic Details
Main Authors: Tesfaye Adugna, Wenbo Xu, Jinlong Fan, Haitao Jia, Xin Luo
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Subjects:
Convolutional neural networks (CNNs)
coarse resolution
deep learning
land cover
sematic segmentation
U-net
Online Access:https://ieeexplore.ieee.org/document/10696947/
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