Comparing ANN and Other Machine Learning Algorithms in the Study of Feature Classification Based on Multi-spectral Remote Sensing
Year: 2024
Type: article
Abstract: Due to the complexity and diversity of features, accurately recognizing their classification accuracy is significant for remote sensing data processing. Machine learning algorithms have high accuracy and can be applied to a variety of classification problems, providing excellent solutions to the problem of feature classification. In order to achieve fast and accurate classification of features and screen out the best model, this paper takes forests and airplanes as an example and compares and analyzes the accuracy effects of Support Vector Machine (SVM), Random Forest (RF), Artificial Neural Network (ANN) and AdaBoost (Adaptive Boosting) on feature classification based on multi-spectral remote sensing images collected by aerial. The results show that among the four models, SVM has the highest average accuracy, the best stability, and the fastest solution time, and can be used as the optimal model. ANN and AdaBoost are the next best, and RF is the worst. This paper can provide reference and guidance for feature classification model selection.
DOI: 10.62051/1xnppb51
Author: Shangming Zhang
Institution: University of Shanghai for Science and Technology
Language: English
Subfield: Media Technology
Field: Engineering
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4401553087