Despeckling of Multitemporal Sentinel SAR Images and Its Impact on Agricultural Area Classification
InTech eBooks2018
Citations Over TimeTop 10% of 2018 papers
Vladimir Lukin, Oleksii Rubel, Ruslan Kozhemiakin, Sergey Abramov, Андрій Шелестов, Mykola Lavreniuk, Mykola Meretsky, Benoît Vozel, Kacem Chehdi
Abstract
This chapter addresses an important practical task of classification of multichannel remote sensing data with application to multitemporal dual-polarization Sentinel radar images acquired for agricultural regions in Ukraine. We first consider characteristics of dual-polarization Sentinel radar images and discuss what kind of filters can be applied to such data. Several examples of denoising are presented with analysis of what properties of filters are desired and what can be provided in practice. It is also demonstrated that the use of preliminary denoising produces improvement of classification accuracy where despeckling that is more efficient in terms of standard filtering criteria results in better classification.
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