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Lossy compression of multichannel remote sensing images with quality control

Abstract : Lossy compression is widely used to decrease the size of multichannel remote sensing data. Alongside this positive effect, lossy compression may lead to a negative outcome as making worse image classification. Thus, if possible, lossy compression should be carried out carefully, controlling the quality of compressed images. In this paper, a dependence between classification accuracy of maximum likelihood and neural network classifiers applied to three-channel test and real-life images and quality of compressed images characterized by standard and visual quality metrics is studied. The following is demonstrated. First, a classification accuracy starts to decrease faster when image quality due to compression ratio increasing reaches a distortion visibility threshold. Second, the classes with a wider distribution of features start to “take pixels” from classes with narrower distributions of features. Third, a classification accuracy might depend essentially on the training methodology, i.e., whether features are determined from original data or compressed images. Finally, the drawbacks of pixel-wise classification are shown and some recommendations on how to improve classification accuracy are given. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.
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Submitted on : Friday, May 28, 2021 - 11:17:51 AM
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V. Lukin, I. Vasilyeva, S. Krivenko, F. Li, S. Abramov, et al.. Lossy compression of multichannel remote sensing images with quality control. Remote Sensing, MDPI, 2020, 12 (22), pp.1-35. ⟨10.3390/rs12223840⟩. ⟨hal-03040367⟩



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