Visual Attention Model Based Vehicle Target Detection in Synthetic Aperture Radar Images: A Novel Approach
Visual Attention Model Based Vehicle Target Detection in Synthetic Aperture Radar Images: A Novel Approach
复制标题
合成孔径雷达图像中基于视觉注意模型的车辆目标检测：一种新方法
DOI：
10.1007/s12559-014-9312-x
复制
发表时间：
2014-12
影响因子：
5.4
通讯作者：
Hussain, Amir
中科院分区：
文献类型：
--
作者：
Wang, Jun;Sun, Jinping;Yang, Erfu;Hussain, Amir
尽管可用于此类任务的神经元硬件有限，但人类视觉系统（HVS）具有卓越的实时复杂场景分析能力。 HVS 通过选择潜在的感兴趣区域并减少传输到高级视觉处理的数据量，成功克服了信息瓶颈问题。另一方面，许多人造系统也面临着同样的问题，但未能达到令人满意的性能。其中，基于合成孔径雷达的自动目标识别（SAR-ATR）系统是典型的系统，其采用的传统检测算法被称为恒定误报率（CFAR）。众所周知，它的检测概率 (PD) 很低，并且消耗太多时间。视觉注意模型（VAM）是一种计算模型，旨在模仿 HVS 来预测人类会看向哪里。因此，将VAM应用于SAR-ATR系统有助于解决复杂大量数据的有效实时处理问题。在本文中，我们提出了一种基于VAM的SAR图像车辆目标检测新算法。该算法根据SAR图像中目标检测的要求，对著名的Itti模型进行了修改。修改后的 Itti 模型定位 SAR 图像中的显着区域，并通过使用先验知识进行自上而下的处理来减少误报。使用真实的SAR数据证明了该算法的有效性和有效性，并与传统的CFAR算法进行了对标。仿真结果表明，在局放、误报数量和计算时间方面的性能相对提高。
The human visual system (HVS) possesses a remarkable ability of real-time complex scene analysis despite the limited neuronal hardware available for such tasks. The HVS successfully overcomes the problem of information bottleneck by selecting potential regions of interest and reducing the amount of data transmitted to high-level visual processing. On the other hand, many man-made systems are also confronted with the same problem yet fail to achieve satisfactory performance. Among these, the synthetic aperture radar-based automatic target recognition (SAR-ATR) system is a typical one, where the traditional detection algorithm employed is termed the constant false alarm rate (CFAR). It is known to exhibit a low probability of detection (PD) and consumes too much time. The visual attention model (VAM) is a computational model, which aims to imitate the HVS in predicting where humans will look. The application of VAM to the SAR-ATR system could thus help solve the problem of effective real-time processing of complex large amounts of data. In this paper, we propose a new vehicle target detection algorithm for SAR images based on the VAM. The algorithm modifies the well-known Itti model according to the requirements of target detection in SAR images. The modified Itti model locates salient regions in SAR images and following top-down processing reduces false alarms by using prior knowledge. Real SAR data are used to demonstrate the validity and effectiveness of the proposed algorithm, which is also benchmarked against the traditional CFAR algorithm. Simulation results show comparatively improved performance in terms of PD, number of false alarms and computing time.