Research on Farmland Extraction from Remote Sensing Images Based on Decision Tree

Liang Wu, LanPing Xiao

2022

Abstract

In recent years, along with the rapid development of science and technology and the rapid increase of China’s population, urbanization has become more and more serious, and the decrease of arable land will directly lead to food crisis and thus social problems, therefore, the statistical monitoring of arable land area is especially important. In this paper, we propose a random forest-based construction of multiple decision tree model to segment and extract remote sensing image plots for research. In this paper, a graph theory-based segmentation method is used for image segmentation, and the Canny edge operator is introduced to extract edge information, which is used to suppress the over-segmentation phenomenon generated by it. Next, it is optimized using a Bagging-based random forest expansion algorithm. We conducted experiments on the hyperspectral remote sensing image number dataset captured by the Resource 3 (ZY-3) satellite provided by MathorCup, and finally obtained an accuracy of 88.09%.

Paper Citation

in Harvard Style

Wu L. and Xiao L. (2022). Research on Farmland Extraction from Remote Sensing Images Based on Decision Tree. In Proceedings of the 3rd International Symposium on Automation, Information and Computing - Volume 1: ISAIC; ISBN 978-989-758-622-4, SciTePress, pages 209-215. DOI: 10.5220/0011918000003612

in Bibtex Style

@conference{isaic22,
author={Liang Wu and LanPing Xiao},
title={Research on Farmland Extraction from Remote Sensing Images Based on Decision Tree},
booktitle={Proceedings of the 3rd International Symposium on Automation, Information and Computing - Volume 1: ISAIC},
year={2022},
pages={209-215},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011918000003612},
isbn={978-989-758-622-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 3rd International Symposium on Automation, Information and Computing - Volume 1: ISAIC
TI - Research on Farmland Extraction from Remote Sensing Images Based on Decision Tree
SN - 978-989-758-622-4
AU - Wu L.
AU - Xiao L.
PY - 2022
SP - 209
EP - 215
DO - 10.5220/0011918000003612
PB - SciTePress