{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T16:15:08Z","timestamp":1740154508488,"version":"3.37.3"},"reference-count":60,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2020,11,23]],"date-time":"2020-11-23T00:00:00Z","timestamp":1606089600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"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 \u201ctake pixels\u201d 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.<\/jats:p>","DOI":"10.3390\/rs12223840","type":"journal-article","created":{"date-parts":[[2020,11,23]],"date-time":"2020-11-23T13:18:23Z","timestamp":1606137503000},"page":"3840","source":"Crossref","is-referenced-by-count":18,"title":["Lossy Compression of Multichannel Remote Sensing Images with Quality Control"],"prefix":"10.3390","volume":"12","author":[{"given":"Vladimir","family":"Lukin","sequence":"first","affiliation":[{"name":"Department of Information and Communication Technologies, National Aerospace University, 61070 Kharkov, Ukraine"}]},{"given":"Irina","family":"Vasilyeva","sequence":"additional","affiliation":[{"name":"Department of Information and Communication Technologies, National Aerospace University, 61070 Kharkov, Ukraine"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6027-5442","authenticated-orcid":false,"given":"Sergey","family":"Krivenko","sequence":"additional","affiliation":[{"name":"Department of Information and Communication Technologies, National Aerospace University, 61070 Kharkov, Ukraine"}]},{"given":"Fangfang","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Information and Communication Technologies, National Aerospace University, 61070 Kharkov, Ukraine"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8295-9439","authenticated-orcid":false,"given":"Sergey","family":"Abramov","sequence":"additional","affiliation":[{"name":"Department of Information and Communication Technologies, National Aerospace University, 61070 Kharkov, Ukraine"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6206-3988","authenticated-orcid":false,"given":"Oleksii","family":"Rubel","sequence":"additional","affiliation":[{"name":"Department of Information and Communication Technologies, National Aerospace University, 61070 Kharkov, Ukraine"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1920-2847","authenticated-orcid":false,"given":"Benoit","family":"Vozel","sequence":"additional","affiliation":[{"name":"Institut d\u2019\u00c9lectronique et des Technologies du num\u00e9Rique, University of Rennes 1, UMR CNRS 6164, 22300 Lannion, France"}]},{"given":"Kacem","family":"Chehdi","sequence":"additional","affiliation":[{"name":"Institut d\u2019\u00c9lectronique et des Technologies du num\u00e9Rique, University of Rennes 1, UMR CNRS 6164, 22300 Lannion, France"}]},{"given":"Karen","family":"Egiazarian","sequence":"additional","affiliation":[{"name":"Computational Imaging Group, Tampere University, 33720 Tampere, Finland"}]}],"member":"1968","published-online":{"date-parts":[[2020,11,23]]},"reference":[{"key":"ref_1","unstructured":"Mielke, C., Boshce, N.K., Rogass, C., Segl, K., Gauert, C., and Kaufmann, H. 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