Browsing by Author "Merlet, T."
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Item Open Access SAR image segmentation with GMMs(IET, 2017-10) Belloni, C.; Aouf, Nabil; Merlet, T.; Le Caillec, J MThis paper proposes a new approach for Synthetic Aperture Radar (SAR) image segmentation. Segmenting SAR images can be challenging because of the blurry edges and the high speckle. The segmentation proposed is based on a machine learning technique. Gaussian Mixture Models (GMMs) were already used to segment images in the visual field and are here adapted to work with single channel SAR images. The segmentation suggested is designed to be a first step towards feature and model based classification. The recall rate is the most important as the goal is to retain most target’s features. A high recall rate of 88%, higher than for other segmentation methods on the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset, was obtained. The next classification stage is thus not affected by a lack of information while its computation load drops. With this method, the inclusion of disruptive features in models of targets is limited, providing computationally lighter models and a speed up in further classification as the narrower segmented areas foster convergence of models and provide refined features to compare. This segmentation method is hence an asset to template, feature and model based classification methods. Besides this method, a comparison between variants of the GMMs segmentation and a classical segmentation is provided.Item Open Access Study of the Frequency Slope Effect on the Chirp Waveform Orthogonality(IET, 2017-10-26) Kocjancic, Leon; Balleri, Alessio; Merlet, T.Multiple-Beam Radar Systems (MBRS) based on waveform diversity require a set of orthogonal waveforms in order to generate multiple channels in transmission and extract them efficiently at the receiver using digital signal processing. Chirp signals are extensively used in radar systems due to their pulse compression properties, Doppler tolerance and ease of generation. In this paper, we investigate the level of isolation between two linear frequency modulated (LFM) chirps as a function of the frequency slope and of the chirp starting frequency. Results are derived analytically and verified with a set of measurements at S-band.