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ISSN 0474-8662. Information Extraction and Processing. 2023. Issue 51 (127)
Classification of remote sensing images based on multi-threshold binarization
Rusyn B. P.
Karpenko Physico-Mechanical Institute of the NAS of Ukraine, Lviv
Lutsyk O. A.
Karpenko Physico-Mechanical Institute of the NAS of Ukraine, Lviv
Kosarevych R. Ya.
Karpenko Physico-Mechanical Institute of the NAS of Ukraine, Lviv
Korniy V. V.
Karpenko Physico-Mechanical Institute of the NAS of Ukraine, Lviv
https://doi.org/10.15407/vidbir2023.51.062
Keywords: multi-spectral image, multi-threshold binarization, classification.
Cite as: Rusyn B. P., Lutsyk O. A., Kosarevych R. Ya., Korniy V. V. Classification of remote sensing images based on multi-threshold binarization. Information Extraction and Processing. 2023, 51(127), 62-69. DOI:https://doi.org/10.15407/vidbir2023.51.062
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Abstract
In the field of remote sensing, the classification and recognition of multi-spectral images play a key role in understanding and monitoring our environment. This paper introduces an innovative method that leverages multi-threshold binarization for feature extraction in the classification and recognition of multi-spectral images. The core idea of this approach is to represent the intricate characteristics of multi-spectral images through a series of binary attributes, each of which captures specific image properties. One of the key findings of this research is the corre lation between the size of the training dataset and the performance accuracy of the classifier trained on this dataset. This relationship shows on the importance of having an appropriately sized training dataset to achieve high classification precision. Moreover, the approach offers a significant advantage in terms of efficiency compared to traditional convolutional neural network-based training processes. The reduced training time and faster operation of the method open up exciting possibilities for real-time recognition and classification of multi-spectral images. To validate the effectiveness of the approach, we conducted experiments on various test training datasets of multi-spectral images, and the results of these evaluations are presented. The findings highlight the promising potential of this method for a wide range of applications, including environmental monitoring, urban planning, and agricultural management, where accurate and real-time classification is essential. It is proposed a novel approach to multi-spectral image classification, employing multi-threshold binarization for feature extraction. The method s ability to represent image features as binary characteristics offers a fresh perspective in the field of remote sensing. The findings of this research not only contribute to advancing the state of the art in multi-spectral image analysis but also provide a practical and efficient solution for real-time recognition and classification, aligning with the growing demands of various applications.
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