MINACE filter classification algorithms for ATR using MSTAR data
MINACE filter classification algorithms for ATR using MSTAR data
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DOI：
10.1117/12.603065
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发表时间：
2005-05
期刊：
影响因子：
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通讯作者：
R. Patnaik;D. Casasent
中科院分区：
文献类型：
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作者：
R. Patnaik;D. Casasent
提出了一种基于最小噪声和相关能量（MINACE）畸变不变滤波器（DIF）的合成孔径雷达（SAR）自动目标识别（ATR）系统。一组MINACE过滤器覆盖不同的方面范围合成每个对象使用该对象的图像的训练集和一个验证集的混淆和杂波图像。以前DIF工作没有解决混淆拒绝问题。我们还解决了使用更少的DIF每个对象比以前的工作。使用训练集和验证集自动为每个滤波器选择MINACE滤波器参数c。该系统使用来自移动和静止目标获取和识别（MSTAR）公共数据库的图像进行评估。的分类分数（PC）和混淆和杂波（PFA和PCFA分别）的虚警分数的数量为基准的三类MSTAR数据库与对象的变体和两个混淆。输入的测试图像的姿态是不假设是已知的，因此解决的问题是更现实的比在以前的工作，因为SAR对象的姿态估计有很大的误差幅度。干扰和杂波抑制的结果。
A synthetic aperture radar (SAR) automatic target recognition (ATR) system based on the minimum noise and correlation energy (MINACE) distortion-invariant filter (DIF) is presented. A set of MINACE filters covering different aspect ranges is synthesized for each object using a training set of images of that object and a validation set of confuser and clutter images. No prior DIF work addressed confuser rejection. We also address use of fewer DIFs per object than prior work did. The selection of the MINACE filter parameter c for each filter is automated using training and validation sets. The system is evaluated using images from the Moving and Stationary Target Acquisition and Recognition (MSTAR) public database. The classification scores (PC) and the number of false alarm scores for confusers and clutter (PFA and PCFA respectively) are presented for the benchmark three-class MSTAR database with object variants and two confusers. The pose of the input test image is not assumed to be known, thus the problem addressed is more realistic than in prior work, since pose estimation of SAR objects has a large margin of error. Results for both confuser and clutter rejection are presented.