Impact of SAR data filtering on crop classification accuracy

For many applied problems in agricultural monitoring and food security, it is important to provide reliable crop classification maps. In this paper, we aim to compare performance of different filters available in ESA SNAP toolbox and compare them with our approach with applying to reduce speckle in multitemporal synthetic-aperture radar (SAR) Sentinel-1 imagery. For this, we evaluate an impact of SAR data filtering on crop classification accuracy. We have found that overall classification accuracy without any filtering is 82.6% whilst the use of different despeckling methods achieves gain of crop map accuracy from +3.2% to +5.1% compared to classification of original data. The most accurate crop map has been obtained for SAR images pre-processed by DCT-based filter.