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Morphological Operators for Polarimetric Anomaly Detection

机译:形态学算子用于极化异常检测

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摘要

We introduce an algorithm of morphological filters and propose its use to classic polarization metrics for applications requiring passive longwave-infrared, polarimetric remote sensing and real-time anomaly detection. The approach significantly augments the daytime and nighttime detectability of weak-signal manmade objects immersed in a predominant natural background scene. A tailored sequence of signal-enhancing filters is featured, consisting of basic and higher level morphological operators to achieve a desired goal. Qualitatively, the goal is to effectively squeeze the variance of pixel values representing the natural clutter background, while simultaneously spreading the pixel variance within the manmade object class and separating the pixel mean averages between the two classes of objects. Using real data, the approach persistently detected with a high confidence level three mobile military howitzer surrogates (targets) from natural clutter, during a 72-h coverage. Targets were posed at three aspect angles (range 557 m), yielding a negligible false alarm rate. Performance was invariant to diurnal cycle and mild atmospheric changes.
机译:我们介绍了一种形态学过滤器算法,并将其用于经典极化度量标准,以用于需要被动长波红外,极化遥感和实时异常检测的应用。该方法显着增强了浸没在主要自然背景场景中的弱信号人造物体在白天和晚上的可检测性。量身定制的信号增强滤波器序列,由基本和高级形态运算符组成,以实现所需的目标。定性地,目标是有效地压缩代表自然杂波背景的像素值的方差,同时在人造对象类中扩展像素方差,并在两类对象之间分离像素均值。使用真实数据,在72小时的覆盖范围内,该方法以高置信度持续检测到三个移动军用榴弹炮从自然杂波中进行替代(目标)。以三个纵横比(范围557 m)放置目标,产生的误报率可忽略不计。性能对于昼夜周期和轻微的大气变化是不变的。

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