TDMSANet: A Tri-Dimensional Multi-Head Self-Attention Network for Improved Crop Classification from Multitemporal Fine-Resolution Remotely Sensed Images

TDMSANet: A Tri-Dimensional Multi-Head Self-Attention Network for Improved Crop Classification from Multitemporal Fine-Resolution Remotely Sensed Images

Accurate and timely crop distribution data are crucial for governments, in order to make related policies to ensure food security. However, agricultural ecosystems are spatially and temporally dynamic systems, which poses a great challenge for accurate crop mapping using fine spatial resolution (FSR...

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Bibliographic Details
Main Authors: Jian Li, Xuhui Tang, Jian Lu, Hongkun Fu, Miao Zhang, Jujian Huang, Ce Zhang, Huapeng Li
Format: Article
Language:English
Published: MDPI AG 2024-12-01
Series:Remote Sensing
Subjects:
crop mapping
deep learning
fine spatial resolution imagery
image time series
multi-head self-attention
spatial feature extraction
Online Access:https://www.mdpi.com/2072-4292/16/24/4755
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