Browsing by Author "Belloni, Carole"

Now showing 1 - 4 of 4
  • Thumbnail Image
    ItemOpen Access
    Automatic x-ray image segmentation and clustering for threat detection
    (SPIE, 2017-10-05) Kechagias-Stamatis, Odysseas; Aouf, Nabil; Nam, David; Belloni, Carole
    Firearms currently pose a known risk at the borders. The enormous number of X-ray images from parcels, luggage and freight coming into each country via rail, aviation and maritime presents a continual challenge to screening officers. To further improve UK capability and aid officers in their search for firearms we suggest an automated object segmentation and clustering architecture to focus officers’ attentions to high-risk threat objects. Our proposal utilizes dual-view single/ dual-energy 2D X-ray imagery and is a blend of radiology, image processing and computer vision concepts. It consists of a triple-layered processing scheme that supports segmenting the luggage contents based on the effective atomic number of each object, which is then followed by a dual-layered clustering procedure. The latter comprises of mild and a hard clustering phase. The former is based on a number of morphological operations obtained from the image-processing domain and aims at disjoining mild-connected objects and to filter noise. The hard clustering phase exploits local feature matching techniques obtained from the computer vision domain, aiming at sub-clustering the clusters obtained from the mild clustering stage. Evaluation on highly challenging single and dual-energy X-ray imagery reveals the architecture’s promising performance.
  • Thumbnail Image
    ItemOpen Access
    Explainability of deep SAR ATR through feature analysis
    (IEEE, 2020-10-20) Belloni, Carole; Aouf, Nabil; Balleri, Alessio; Le Caillec, Jean-Marc; Merlet, Thomas
    Understanding the decision-making process of deep learning networks is a key challenge which has rarely been investigated for Synthetic Aperture Radar (SAR) images. In this paper, a set of new analytical tools is proposed and applied to a Convolutional Neural Network (CNN) handling Automatic Target Recognition (ATR) on two SAR datasets containing military targets.
  • Thumbnail Image
    ItemOpen Access
    Pose-informed deep learning method for SAR ATR
    (The institution of Engineering and Technology (IET), 2020-03-30) Belloni, Carole; Aouf, Nabil; Balleri, Alessio; Le Caillec, Jean-Marc; Merlet, Thomas
    Synthetic aperture radar (SAR) images for automatic target classification (automatic target recognition (ATR)) have attracted significant interest as they can be acquired day and night under a wide range of weather conditions. However, SAR images can be time consuming to analyse, even for experts. ATR can alleviate this burden and deep learning is an attractive solution. A new deep learning Pose-informed architecture solution, that takes into account the impact of target orientation on the SAR image as the scatterers configuration changes, is proposed. The classification is achieved in two stages. First, the orientation of the target is determined using a Hough transform and a convolutional neural network (CNN). Then, classification is achieved with a CNN specifically trained on targets with similar orientations to the target under test. The networks are trained with translation and SAR-specific data augmentation. The proposed Pose-informed deep network architecture was successfully tested on the Military Ground Target Dataset (MGTD) and the Moving and Stationary Target Acquisition and Recognition (MSTAR) datasets. Results show the proposed solution outperformed standard AlexNets on the MGTD, MSTAR extended operating condition (EOC)1, EOC2 and standard operating condition (SOC)10 datasets with a score of 99.13% on the MSTAR SOC10.
  • Thumbnail Image
    ItemOpen Access
    SAR image dataset of military ground targets with multiple poses for ATR
    (SPIE, 2017-10-05) Belloni, Carole; Balleri, Alessio; Aouf, Nabil; Merlet, Thomas; Le Caillec, Jean-Marc
    Automatic Target Recognition (ATR) is the task of automatically detecting and classifying targets. Recognition using Synthetic Aperture Radar (SAR) images is interesting because SAR images can be acquired at night and under any weather conditions, whereas optical sensors operating in the visible band do not have this capability.Existing SAR ATR algorithms have mostly been evaluated using the MSTAR dataset.1 The problem with the MSTAR is that some of the proposed ATR methods have shown good classification performance even when targets were hidden,2 suggesting the presence of a bias in the dataset. Evaluations of SAR ATR techniques arecurrently challenging due to the lack of publicly available data in the SAR domain. In this paper, we present a high resolution SAR dataset consisting of images of a set of ground military target models taken at various aspect angles, The dataset can be used for a fair evaluation and comparison of SAR ATR algorithms. We applied the Inverse Synthetic Aperture Radar (ISAR) technique to echoes from targets rotating on a turntable and illuminated with a stepped frequency waveform. The targets in the database consist of four variants of two 1.7m-long models of T-64 and T-72 tanks. The gun, the turret position and the depression angle are varied to form 26 different sequences of images. The emitted signal spanned the frequency range from 13 GHz to 18 GHz to achieve a bandwidth of 5 GHz sampled with 4001 frequency points. The resolution obtained with respect to the size of the model targets is comparable to typical values obtained using SAR airborne systems. Single polarized images (Horizontal-Horizontal) are generated using the backprojection algorithm.3 A total of 1480 images are produced using a 20° integration angle. The images in the dataset are organized in a suggested training and testing set to facilitate a standard evaluation of SAR ATR algorithms.