In this paper we present a comparison of the robustness of Convolutional Neural Networks (CNN) to other classifiers in the presence of uncertainty of the objects localization in SAR image. We present a framework for simulating simple SAR images, translating the object of interest systematically and testing the classification performance. Our results show that where other classification methods are very sensitive to even small translations, CNN is quite robust to translational variance, making it much more useful in relation to Automatic Target Recognition (ATR) in a real life context.
|Title of host publication||Proceedings of EUSAR 2016: 11th European Conference on Synthetic Aperture Radar|
|Publication status||Published - 2016|
|Event||11th European Conference on Synthetic Aperture Radar (EUSAR 2016) - Hamburg, Germany|
Duration: 6 Jun 2016 → 9 Jun 2016
Conference number: 11
|Conference||11th European Conference on Synthetic Aperture Radar (EUSAR 2016)|
|Period||06/06/2016 → 09/06/2016|