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Deep learning and featured-based classification techniques for radar imagery

Carole Belloni 1, 2
Lab-STICC - Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance
Abstract : Autonomous moving platforms carrying radar systems can synthesise long antenna apertures and generate Synthetic Aperture Radar (SAR) images. SAR images provide strategic information for military and civilian applications and they can be acquired day and night under a wide range of weather conditions. Because the interpretation of SAR images is a common challenge, Automatic Target Recognition (ATR) algorithms can help assist with decision-making when the operator is in the loop or when the platforms are fully autonomous. One of the main limitations of developing SAR ATR algorithms is the lack of suitable and publicly available data. Optical images classification, instead, has recently attracted significantly more research interest because of the number of potential applications and the profusion of data. As a result, robust feature-based and deep learning classification methods have been developed for optical imaging that could be applied to the SAR domain. In this thesis, a new Inverse SAR (ISAR) dataset consisting of test and training images acquired under a range of geometrical conditions is presented. In addition, a method is proposed to generate extra synthetic images, by simulating realistic SAR noise on the original images, and increase the training efficiency of classification algorithms that require a wealth of data, such as deep neural networks. A Gaussian Mixture Model (GMM) segmentation approach is adapted to segment single-polarised SAR images of targets. Features proposed to characterise optical images are transferred to the SAR domain to carry out target classification after segmentation and their respective performanceis compared. A new pose-informed deep learning network architecture, that takes into account the effects of target orientation on target appearance in a SAR image, is proposed. The results presented in this thesis show that the use of this architecture provides a significant performance improvement for almost all datasets used in this work over a baseline network. Understanding the decision-making process of deep networks is another key challenge of deep learning. To address this issue, a new set of analytical tools is proposed that enables the identification, amongst other things, of the location of the algorithm focus points that lead to high level classification performance.
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Submitted on : Tuesday, September 22, 2020 - 11:51:08 AM
Last modification on : Wednesday, October 14, 2020 - 4:09:38 AM


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  • HAL Id : tel-02945414, version 1


Carole Belloni. Deep learning and featured-based classification techniques for radar imagery. Computer Vision and Pattern Recognition [cs.CV]. Ecole nationale supérieure Mines-Télécom Atlantique, 2019. English. ⟨NNT : 2019IMTA0164⟩. ⟨tel-02945414⟩



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