Research Institute of Electronic Engineering Technology, Harbin Institute of Technology, Harbin, Heilongjiang, China.
Department of Electrical and Electronic Engineering, Imperial College, London, UK.
Comput Intell Neurosci. 2017;2017:7186120. doi: 10.1155/2017/7186120. Epub 2017 Nov 29.
A strategy is introduced for achieving high accuracy in synthetic aperture radar (SAR) automatic target recognition (ATR) tasks. Initially, a novel pose rectification process and an image normalization process are sequentially introduced to produce images with less variations prior to the feature processing stage. Then, feature sets that have a wealth of texture and edge information are extracted with the utilization of wavelet coefficients, where more effective and compact feature sets are acquired by reducing the redundancy and dimensionality of the extracted feature set. Finally, a group of discrimination trees are learned and combined into a final classifier in the framework of Real-AdaBoost. The proposed method is evaluated with the public release database for moving and stationary target acquisition and recognition (MSTAR). Several comparative studies are conducted to evaluate the effectiveness of the proposed algorithm. Experimental results show the distinctive superiority of the proposed method under both standard operating conditions (SOCs) and extended operating conditions (EOCs). Moreover, our additional tests suggest that good recognition accuracy can be achieved even with limited number of training images as long as these are captured with appropriately incremental sample step in target poses.
提出了一种用于实现高准确度合成孔径雷达（SAR）自动目标识别（ATR）任务的策略。首先，引入了一种新颖的姿态校正过程和图像归一化过程，以在特征处理阶段之前生成变化较少的图像。然后，利用小波系数提取具有丰富纹理和边缘信息的特征集，通过减少提取特征集的冗余和维数，获得更有效和紧凑的特征集。最后，在 Real-AdaBoost 的框架中学习一组判别树并将其组合成最终分类器。使用公开的运动和静止目标获取和识别（MSTAR）数据库对所提出的方法进行评估。进行了几项比较研究以评估所提出算法的有效性。实验结果表明，在所提出的方法下，在标准操作条件（SOC）和扩展操作条件（EOC）下都具有明显的优势。此外，我们的附加测试表明，只要以适当的增量样本步长在目标姿态中捕获训练图像，即使训练图像数量有限，也可以获得良好的识别精度。
