Quantum Machine Learning for Optical and SAR Classification

We present in this paper a method to compare scene classification accuracy of C-band Synthetic aperture radar (SAR) and optical images utilizing both classical and quantum computing algorithms. This REU study uses data from the Sentinel satellite. The dataset contains (i) synthetic aperture radar images collected from the Sentinel-1 satellite and (ii) optical images for the same area as the SAR images collected from the Sentinel-2 satellite. We examine classical neural networks to classify four classes of images. We then explore Quantum Convolutional Neural Networks and deep learning techniques in terms of their training and classification performance. A hybrid Quantum-classical model that is trained on the Sentinel1-2 dataset is proposed, and its performance is then compared against the classical model in terms of classification accuracy.