Mapping dominant tree species in western Yunnan by comparing three feature filtering methods

Peiwei Liu

doi:10.1117/12.3009116

16 October 2023 Mapping dominant tree species in western Yunnan by comparing three feature filtering methods

Peiwei Liu

Proceedings Volume 12803, Fifth International Conference on Artificial Intelligence and Computer Science (AICS 2023); 128032F (2023) https://doi.org/10.1117/12.3009116
Event: 2023 5th International Conference on Artificial Intelligence and Computer Science (AICS 2023), 2023, Wuhan, China

Abstract

Forests play an indispensable role in the terrestrial biosphere by maintaining the global carbon cycle, preserving species diversity, and regulating the climate environment. The dominant tree species mapping in Yunnan is a crucial tool for reflecting the distribution of forest resources in the region. Feature engineering is very important in traditional machine learning. It primarily involves two demands: firstly, when there are too many feature dimensions, feature selection is performed to speed up model training by filtering out unimportant features. Secondly, when there are fewer features or poor model training performance, feature construction can be attempted to enhance dimensionality by understanding the problem. In this study, the study area, covering 78% of the forest in western Yunnan (approximately 45,000 km²), utilized the GEE (Google Earth Engine) cloud computing platform to combine long time series of Sentinel-2 images, topographic data, and environmental data to obtain 100-dimensional features. Three feature filtering methods, including Lasso, relevance hierarchical clustering, and feature recursive elimination, were used to filter the features, and three machine learning classifiers, including Random Forest (RF), eXtreme Gradient Boosting (XGBoost), and Support vector machine (SVM), were used to obtain nine 10-meter-long map products. The results showed an overall accuracy of 76.4% for the 9 tree categories. Feature filtering improved the overall accuracy rate by 1% to 2% and significantly increased the efficiency of the machine learning process.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Peiwei Liu "Mapping dominant tree species in western Yunnan by comparing three feature filtering methods", Proc. SPIE 12803, Fifth International Conference on Artificial Intelligence and Computer Science (AICS 2023), 128032F (16 October 2023); https://doi.org/10.1117/12.3009116

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KEYWORDS

Tunable filters

Machine learning

Remote sensing

Feature selection

Digital filtering

Image processing

Image classification

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