Lossy Compression of Multichannel Remote Sensing Images with Quality Control

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

doi:10.3390/rs12223840

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.

Highlights

Nowadays, remote sensing (RS) is used in numerous applications [1,2,3] due to the following main reasons
If a classifier is trained for original data and applied to compressed images, more compression leads to classification worsening; the difference in Pcc and probabilities of correct classification for particular classes can be negligible; classification accuracy for some particular classes can even improve due to noise/variation suppression; 3
It is worth training a classifier for “conditions” they will be applied; these conditions can be described by quality metrics’ values that are planned to be provided at compression stage or quantization step (QS) value that will be used; 4

Summary

Introduction

Remote sensing (RS) is used in numerous applications [1,2,3] due to the following main reasons. Different types of useful information can be potentially retrieved from RS images, especially high resolution and multichannel data (i.e., a set of co-registered component images of the same territory acquired for different wavelengths, polarizations, even by different sensors [3,4,5,6]). The volume of RS data greatly increases due to the aforementioned factors: better spatial resolution, a larger number of channels, more frequent observations. This causes challenges in RS data processing that relate to all basic stages of their processing: co-registration, calibration, pre- or post-filtering, compression, segmentation, and classification [9,10]. Lossless compression is often unable to meet requirements to compression ratio (CR) that should be provided, since, even in the most favorable situations of high inter-band correlation of component images [14], CR attained by the best existing lossless compression techniques reaches 4 . . . 5 [12]

Objectives

Results

Discussion

Conclusion