Crop classification method using multifeature optimal selection combined with an ensemble learning model: a case study of the Sanjiang Plain

Linsheng Huang; Qibao Lu; Wenjiang Huang; Jinsong Li

doi:10.1117/12.3035664

10 July 2024 Crop classification method using multifeature optimal selection combined with an ensemble learning model: a case study of the Sanjiang Plain

Linsheng Huang, Qibao Lu, Wenjiang Huang, Jinsong Li

Author Affiliations +

Proceedings Volume 13223, Fifth International Conference on Geology, Mapping, and Remote Sensing (ICGMRS 2024); 132230S (2024) https://doi.org/10.1117/12.3035664
Event: 2024 5th International Conference on Geology, Mapping and Remote Sensing (ICGMRS 2024), 2024, Wuhan, China

Abstract

Crop classification is a crucial step in crop monitoring and yield estimation. Integrating multi-source and multi-temporal remote sensing images can effectively enhance the accuracy of crop classification, but it leads to an increase in the dimensionality of features. It is necessary to employ appropriate feature selection methods and classification algorithms to improve the precision and efficiency of the classification. Therefore, this study takes the Sanjiang Plain as an example, combines multi-temporal features of Sentinel-1/2 images, and conducts research on crop classification methods using the XGBoost ensemble learning algorithm through feature selection methods. The research results show that the feature selection combined with the XGBoost algorithm yields the optimal classification results, reducing the feature dimensions from 195 to 33, with spectral features being the most important. The Kappa coefficient reached 0.941, which is 0.024 and 0.059 higher than RF and SVM, respectively. The error in crop area extraction results compared to the official statistical data is less than 14%. This method can accurately and efficiently perform crop classification, providing a reference for crop classification in the Sanjiang Plain and similar regions.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Linsheng Huang, Qibao Lu, Wenjiang Huang, and Jinsong Li "Crop classification method using multifeature optimal selection combined with an ensemble learning model: a case study of the Sanjiang Plain", Proc. SPIE 13223, Fifth International Conference on Geology, Mapping, and Remote Sensing (ICGMRS 2024), 132230S (10 July 2024); https://doi.org/10.1117/12.3035664

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available