Paper Info
Title
Mapping Large-Scale Plateau Forest in Sanjiangyuan Using High-Resolution Satellite Imagery and Few-Shot Learning
Abstract
Monitoring the extent of plateau forests has drawn much attention from governments given the fact that the plateau forests play a key role in global carbon circulation. Despite the recent advances in the remote-sensing applications of satellite imagery over large regions, accurate mapping of plateau forest remains challenging due to limited ground truth information and high uncertainties in their spatial distribution. In this paper, we aim to generate a better segmentation map for plateau forests using high-resolution satellite imagery with limited ground-truth data. We present the first 2 m spatial resolution large-scale plateau forest dataset of Sanjiangyuan National Nature Reserve, including 38,708 plateau forest imagery samples and 1187 handmade accurate plateau forest ground truth masks. We then propose an few-shot learning method for mapping plateau forests. The proposed method is conducted in two stages, including unsupervised feature extraction by leveraging domain knowledge, and model fine-tuning using limited ground truth data. The proposed few-shot learning method reached an F1-score of 84.23%, and outperformed the state-of-the-art object segmentation methods. The result proves the proposed few-shot learning model could help large-scale plateau forest monitoring. The dataset proposed in this paper will soon be available online for the public.
Year
DOI
Venue
2022
10.3390/rs14020388
REMOTE SENSING
Keywords
DocType
Volume
large-scale plateau forest mapping, Sanjiangyuan National Nature Reserve, high resolution satellite imagery, ZY-3, few-shot learning
Journal
14
Issue
Citations 
PageRank 
2
0
0.34
References 
Authors
0
7
Name
Order
Citations
PageRank
Zhihao Wei100.34
Bin Jiang212.38
Xiaowei Jia38021.04
Pengyu Liu400.34
Ying Ma500.34
Ting Chen600.34
Guilian Feng700.34