A machine learning approach for image classification in synthetic aperture RADAR
This addresses classification problems in remote sensing for SAR imagery users, but it is incremental as it applies existing CNNs to SAR data.
The paper tackled object classification in Synthetic Aperture RADAR (SAR) using Convolutional Neural Networks (CNNs), achieving high classification accuracies of at least 75% for both shape classification and ice type identification.
We consider the problem in Synthetic Aperture RADAR (SAR) of identifying and classifying objects located on the ground by means of Convolutional Neural Networks (CNNs). Specifically, we adopt a single scattering approximation to classify the shape of the object using both simulated SAR data and reconstructed images from this data, and we compare the success of these approaches. We then identify ice types in real SAR imagery from the satellite Sentinel-1. In both experiments we achieve a promising high classification accuracy ($\geq$75\%). Our results demonstrate the effectiveness of CNNs in using SAR data for both geometric and environmental classification tasks. Our investigation also explores the effect of SAR data acquisition at different antenna heights on our ability to classify objects successfully.