CASST_Net
- Citation Author(s):
-
songlin yang
- Submitted by:
- Songlin Yang
- Last updated:
- DOI:
- 10.21227/5jzk-g065
9 views
- Categories:
- Keywords:
Abstract
we develop a spatio-spectral-temporal deep learning regression model , termed CASST-Net, which leverages a cosine attention mechanism to enhance feature representation to improve FVC estimation accuracy, can also dynamically adjusted the weighting values of each spectral band in the satellite data based on changes in vegetation physiological characteristics and canopy structure. This end-to-end network model comprises a feature extraction module, which leverages the Swin Transformer and a cosine attention mechanism to capture spatial-spectral-temporal information from multi-temporal unmanned aerial systems (UAS) and satellite data, and a fusion module that serves as a regressor for accurate FVC estimation.
Instructions:
we develop a spatio-spectral-temporal deep learning regression model , termed CASST-Net, which leverages a cosine attention mechanism to enhance feature representation to improve FVC estimation accuracy, can also dynamically adjusted the weighting values of each spectral band in the satellite data based on changes in vegetation physiological characteristics and canopy structure. This end-to-end network model comprises a feature extraction module, which leverages the Swin Transformer and a cosine attention mechanism to capture spatial-spectral-temporal information from multi-temporal unmanned aerial systems (UAS) and satellite data, and a fusion module that serves as a regressor for accurate FVC estimation.
