Features extraction from multi-spectral remote sensing images based on multi-threshold binarization

In this paper, we propose a solution to resolve the limitation of deep CNN models in real-time applications. The proposed approach uses multi-threshold binarization over the whole multi-spectral remote sensing image to extract the vector of discriminative features for classification. We compare the...

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Bibliographic Details
Published in:Scientific reports 2023-11, Vol.13 (1), p.19655-19655, Article 19655
Main Authors: Rusyn, Bohdan, Lutsyk, Oleksiy, Kosarevych, Rostyslav, Maksymyuk, Taras, Gazda, Juraj
Format: Article
Language:English
Subjects:
639/705/1042
639/705/117
704/158
Accuracy
Artificial intelligence
Classification
Datasets
Deep learning
Humanities and Social Sciences
Informatics
multidisciplinary
Neural networks
Remote sensing
Science
Science (multidisciplinary)
Training
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Summary:In this paper, we propose a solution to resolve the limitation of deep CNN models in real-time applications. The proposed approach uses multi-threshold binarization over the whole multi-spectral remote sensing image to extract the vector of discriminative features for classification. We compare the classification accuracy and the training time of the proposed approach with ResNet and Ensemble CNN models. The proposed approach shows a significant advantage in accuracy for small datasets, while keeping very close recall score to both deep CNN models for larger datasets. On the other hand, regardless of the dataset size, the proposed multi-threshold binarization provides approximately 5 times lower training and inference time than both ResNet and Ensemble CNN models.
ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-023-46785-7