Image Classification in Synthetic Aperture Radar Using Reconstruction from Learned Inverse Scattering

Synthetic aperture radar (SAR) is a remote sensing technique used to obtain high-resolution images, where image classification is a primary application. However, reconstructing SAR data is difficult which makes the classification of these images even more challenging. We present a study that investigates techniques for classifying SAR images using machine learning. In particular, we compare the classification accuracy using three different training data: raw SAR observation data, reconstructions using Kirchoff migration, and reconstructions by learning an approximation to the inverse of the SAR sensing operator. We consider two different architectures, namely a multi-layer perceptron and a convolutional neural network. The training set is composed of 50,000 images from the CIFAR-10 dataset. We find that the images reconstructed using the learned approximate inverse have a higher classification accuracy than that from the SAR measurement and the Kirchoff migration approach.