**Vladimir Lukin 1, Irina Vasilyeva 1, Sergey Krivenko 1, Fangfang Li 1, Sergey Abramov 1, Oleksii Rubel 1, Benoit Vozel 2,\*, Kacem Chehdi <sup>2</sup> and Karen Egiazarian <sup>3</sup>**


Received: 25 October 2020; Accepted: 19 November 2020; Published: 23 November 2020

**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.

**Keywords:** remote sensing; lossy compression; image quality; image classification; visual quality metrics
