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) -
FTA-Net: Frequency-Temporal-Aware Network for Remote Sensing Change Detection
by: Taojun Zhu, et al.
Published: (2025-01-01) -
Object-Level Contrastive-Learning-Based Multi-Branch Network for Building Change Detection from Bi-Temporal Remote Sensing Images
by: Shiming Li, et al.
Published: (2025-01-01) -
FBATCNet: A Temporal Convolutional Network With Frequency Band Attention for Decoding Motor Imagery EEG
by: Shuaishuai Ma, et al.
Published: (2025-01-01)