Fine-scale forest classification with multi-temporal sentinel-1/2 imagery using a temporal convolutional neural network
Monitoring and mapping forest vegetation are crucial for conserving biodiversity and estimating biomass and carbon. However, spectral similarity between different vegetation types and the issue of mixed pixels in medium-resolution satellite imagery remain significant challenges for fine-scale forest...
Saved in:
Main Authors: | Rongfei Duan, Chunlin Huang, Peng Dou, Jinliang Hou, Ying Zhang, Juan Gu |
---|---|
Format: | Article |
Language: | English |
Published: |
Taylor & Francis Group
2025-12-01
|
Series: | International Journal of Digital Earth |
Subjects: | |
Online Access: | https://www.tandfonline.com/doi/10.1080/17538947.2025.2457953 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
High-Resolution Geochemical Data Mapping With Swin Transformer-Convolution-Based Multisource Geoscience Data Fusion
by: Ye Yuan, et al.
Published: (2025-01-01) -
TSMGA: Temporal-Spatial Multiscale Graph Attention Network for Remote Sensing Change Detection
by: Xiaoyang Zhang, et al.
Published: (2025-01-01) -
Detection of the Optimal Temporal Windows for Mapping Paddy Rice Under a Double-Cropping System Using Sentinel-2 Imagery
by: Li Sheng, et al.
Published: (2024-12-01) -
FBATCNet: A Temporal Convolutional Network With Frequency Band Attention for Decoding Motor Imagery EEG
by: Shuaishuai Ma, et al.
Published: (2025-01-01) -
Spatial-Temporal Fusion Graph Neural Networks With Mixed Adjacency for Weather Forecasting
by: Ang Guo, et al.
Published: (2025-01-01)