Automatic Target Recognition in Synthetic Aperture Radar image using multiresolution analysis and classifiers combination
Automatic Target Recognition in Synthetic Aperture Radar image using multiresolution analysis and classifiers combination
复制标题
使用多分辨率分析和分类器组合的合成孔径雷达图像自动目标识别
DOI：
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发表时间：
2008
期刊：
影响因子：
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通讯作者：
David Fernandes
中科院分区：
文献类型：
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作者：
Joao Paulo Pordeus Gomes;J. F. B. Brancalion;David Fernandes
自动目标识别（ATR）是国防应用中的一项重要能力。ATR将操作人员从目标获取和分类的过程中移除，减少了对可能威胁的反应时间，并可用于火炮目标交战。提出了一种与目标姿态无关的合成孔径雷达（SAR）图像目标自动识别方法。分类是由三个不同的分类器的最小距离分类器（MDC），二次高斯分类器（QGC）和一个多层感知器（MLP）神经网络的组合，使用投票架构。
Automatic target recognition (ATR) is an important capability for defense application. ATR removes the human operator from the process of target acquisition and classification, reducing the reaction time to possible threats and can be used to gun target engagement. This paper presents one technique used to solve the automatic target recognition problem in synthetic aperture radars (SAR) images, that is independent of target pose in the images. The classification is performed by a combination of three different classifiers the minimum distance classifier (MDC), the quadratic Gaussian classifier (QGC) and a multilayer perceptron (MLP) neural network, using a voting architecture.