Land Classification Using SAR Imagery********

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This project focuses on land classification using Synthetic Aperture Radar imagery provided by MDA Corporation. Radar imaging systems are based on the echo principle: an antenna emits a coherent beam of microwave radiation, which is absorbed, reflected, and backscattered by the Earth's surface. The backscattered portion of the signal is received by the antenna and processed to form an image. The produced image is noisier than optical images but can provide additional information that cannot be easily obtained by optical images. So far MDA Corporation has used the SAR images produced by its satellites with simple pattern recognition methods based on low level image processing. Now, considering the breaking through of deep learning on many different applications, MDA Corporation is interested in evaluating the performance of deep learning models on SAR images. Thus, the aim of this project is to develop, adapt and evaluate the most promising deep learning models for optical images (e.g. Convolutional Neural Networks) on SAR images for land classification.**This project aims to provide MDA with expertise and engineering solutions in order to evaluate the recognition performance of a convolutional neural network. MDA will provide knowledge and know how on SAR images as well as training and validation data. The combination of the data provided by MDA and their expertise on SAR images together with the expertise of my group on visual recognition will provide MDA with excellent conditions to start a new and promising line of research on high-performance recognition on SAR images. ********