Evaluation of the level of accuracy of matching multispectral aerial images through the "Siamese" neural network model (#1808)
Read ArticleDate of Conference
July 17-19, 2024
Published In
"Sustainable Engineering for a Diverse, Equitable, and Inclusive Future at the Service of Education, Research, and Industry for a Society 5.0."
Location of Conference
Costa Rica
Authors
Pacheco-Ramos, Eder David
Dios-Castillo, Christian Abraham
Chavarry-Chankay, Mariana
Abstract
This study evaluates the accuracy of the "Siamese" neural network for matching multispectral aerial images. A set of image data was preprocessed, and the neural network was trained. The precision, recall and F1-score metrics were analyzed, finding that the average precision was 56.35%, with values varying between 25.00% and 91.18%. Recall was more stable with a mean of 52.44%, while precision had a mean of 56.57%. It is concluded that the "Siamese" neural network is effective for matching multispectral aerial images, although the accuracy depends on the training configuration and the characteristics of the data set.