Lossy Compression of Three-Channel Remote Sensing Images with 'Color' Component Downscaling
| Autor: | Makarichev, Victor, Proskura, Galina, Rubel, Oleksii, Lukin, Vladimir V., Vozel, Benoit, Chehdi, Kacem |
|---|---|
| Přispěvatelé: | National Aerospace University, Institut d'Électronique et des Technologies du numéRique (IETR), Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Nantes Université - pôle Sciences et technologie, Nantes Université (Nantes Univ)-Nantes Université (Nantes Univ) |
| Rok vydání: | 2022 |
| Předmět: | |
| Zdroj: | IEEE International Geoscience and Remote Sensing Symposium (IGARSS) IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Jul 2022, Kuala Lumpur, Malaysia. ⟨10.1109/IGARSS46834.2022.9884580⟩ |
| DOI: | 10.1109/igarss46834.2022.9884580 |
| Popis: | International audience; Multichannel systems of remote sensing provide a huge amount of data useful for different applications. However, such images occupy a large space that poses problems of processing, storage, transmission, and management. Lossy compression is widely used to decrease the size of data. In lossy compression, one has to provide a reasonable trade-off between compression ratio (CR) and introduced losses or quality of compressed data. Quality can be characterized in various ways including traditional criteria as peak signal-to-noise ratio (PSNR) or some others as well as criteria that describe efficiency of solving the final tasks of remote sensing as, e.g., probability of correct classification. In this paper, we concentrate on classification of three-channel images that can be either color images or three components of multi- or hyperspectral data acquired, e.g., by Sentinel-2 sensor. In lossy compression of color images, downscaling of color components is often applied to increase CR without essential loss of quality. The goal of this paper is to study the influence of such downscaling on classification accuracy for three-channel remote sensing data. The compression method based on atomic functions is considered since this method allows easy control of compressed image quality and its providing. The neural networks trained for distorted-free images are applied for image classification. Analysis is carried out for four images of different complexity. Based on it, practical recommendations are given. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|