Use of Modified BM3D Filter and CNN Classifier for SAR Data to Improve Crop Classification Accuracy

Monitoring of agricultural regions is an important task. Recent trends to solve it are based on applying multi-temporal remote sensing in order to obtain reliable crop classification maps. If a radar remote sensing is used, speckle presence in the original data reduces a classification accuracy. A negative impact of speckle can be reduced by image prefiltering procedure. Recent studies carried out for Sentinel-1 imagery have shown that more efficient pre-filtering usually results in better classification. Thus, here we propose a modification of block matching three-dimensional (BM3D) filter adapted to the properties of Sentinel-1 radar data. We demonstrate that its use leads to improved classification of crops in agricultural region of Ukraine compared to earlier methods which are based on other filters. Additionally, a convolutional neural network approach for crop mapping is investigated in this paper for decreasing noise impact and is compared with a traditional pixel-based multi-layer perceptron.