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.
Original language | English |
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Article number | 3840 |
Pages (from-to) | 1-35 |
Number of pages | 35 |
Journal | Remote Sensing |
Volume | 12 |
Issue number | 22 |
DOIs | |
Publication status | Published - 23 Nov 2020 |
Publication type | A1 Journal article-refereed |
Keywords
- Image classification
- Image quality
- Lossy compression
- Remote sensing
- Visual quality metrics
Publication forum classification
- Publication forum level 1
ASJC Scopus subject areas
- General Earth and Planetary Sciences