Abstract
Acquired images often have a large size while there can be limitations on communication line capacity and/or storage memory. Then, there is a need to compressed them. If lossy compression is applied, compressed images should have quality enough high for solving the tasks of their further processing as segmentation, classification, object detection. Here, we consider the influence of lossy compression on classification accuracy of three-channel remote sensing images. A specific feature of our analysis is that discrete atomic transform is studied as the basis of lossy compression and maximum likelihood method is applied at classification stage. It is shown that classification accuracy depends on both compression degree that can be characterized in different ways and image complexity. Under certain conditions, classification accuracy remains practically the same as in case of classifying an original (uncompressed) image. Then, it starts to worsen. We show how to provide quite large compression ratio with avoiding sufficient degradation of classification accuracy.
Keywords
- Image lossy compression
- Discrete atomic transform
- Classification accuracy
- Parameter adaptation
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Acknowledgments
The authors acknowledge the funding received from the National Research Foundation of Ukraine within the grant support 2020/01.0273 “Intelligent models and methods for determining land degradation indicators based on satellite data” and 2020.01/0268 "Information technology for fire danger assessment and fire monitoring in natural ecosystems based on satellite data" (NRFU competition "Science for the safety of human and society").
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Makarichev, V., Vasilyeva, I., Lukin, V., Kussul, N., Shelestov, A. (2022). Classification Accuracy of Three-Channel Images Compressed by Discrete Atomic Transform. In: Nechyporuk, M., Pavlikov, V., Kritskiy, D. (eds) Integrated Computer Technologies in Mechanical Engineering - 2021. ICTM 2021. Lecture Notes in Networks and Systems, vol 367. Springer, Cham. https://doi.org/10.1007/978-3-030-94259-5_22
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DOI: https://doi.org/10.1007/978-3-030-94259-5_22
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