Details

Autor(en) / Beteiligte

• Lukin, Vladimir
• Vasilyeva, Irina
• Krivenko, Sergey
• Li, Fangfang
• Abramov, Sergey
• Rubel, Oleksii
• Vozel, Benoit
• Chehdi, Kacem
• Egiazarian, Karen

Titel

Lossy Compression of Multichannel Remote Sensing Images with Quality Control

Ist Teil von

• Remote sensing (Basel, Switzerland), 2020-11, Vol.12 (22), p.3840-35

Ort / Verlag

Basel: MDPI AG

Erscheinungsjahr

2020

Quelle

EZB Electronic Journals Library

Beschreibungen/Notizen

• 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.