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首页> 外文会议>Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XII pt.1 >Signature evolution with covariance equalization in oblique hyperspectral imagery
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Signature evolution with covariance equalization in oblique hyperspectral imagery

机译：斜高光谱图像中具有协方差均衡的签名演化

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摘要

Covariance equalization (CE) is a method by which one can predict the change in an objects's hyperspectral signature due to changes in sun position, atmospheric conditions, and viewing angle and range. Specifically, CE produces a linear transformation that relates the object's signature as measured at the sensor at a particular time to that measured at another time and under different conditions. The transformation is based on the background statistics of a scene imaged at the two times. Although CE was derived under the assumption that the two images cover mostly the same geographic area, it also has been found to work well for objects that have moved from one location to another. The CE technique has been previously verified with data from a nadir-viewing visible hyperspectral camera. In this paper, however, we show results from the application of CE to highly oblique hyperspectral SWIR data. We evaluate the utility of CE primaily through its effectiveness in transforming signatures acquired under one set of conditions for application to matched-filter object detection under a second set of conditions (e.g., view angle, slant range, altitude, atmospheric conditions, and time of day). Object detection with highly oblique sensors (75 deg. to 80 deg. off-nadir) is far more difficult than with nadir-viewing sensors for several reasons: increased atmospheric optical thickness, which results in lower signal-to-noise and higher adjacency effects; fewer pixels on object; the effects of the nonuniformity of the bi-direction reflectance function of most man-made objects; and the change in pixel size when measurements are taken at different slant ranges.
机译：协方差均衡（CE）是一种可以预测由于太阳位置，大气条件以及视角和范围的变化而导致的对象的高光谱特征变化的方法。具体地说，CE产生线性变换，该线性变换将在特定时间在传感器处测得的对象签名与在不同时间和其他时间所测得的签名相关联。转换基于两次成像的场景的背景统计数据。尽管CE是在假设两个图像都覆盖相同地理区域的前提下得出的，但也发现它对于从一个位置移动到另一个位置的对象也很有效。先前已用来自天底观察的可见高光谱相机的数据验证了CE技术。但是，在本文中，我们显示了将CE应用于高度倾斜的高光谱SWIR数据的结果。我们通过评估在一组条件下获得的签名在第二组条件下（例如，视角，倾斜范围，高度，大气条件和飞行时间）应用于转换过滤器物体检测的有效性来评估CE的实用性。天）。与高倾斜传感器相比，高倾斜传感器（距最低点75度至80度）的目标检测要困难得多，原因有以下几个：大气光学厚度增加，这导致较低的信噪比和较高的邻接效应;物体上的像素更少；大多数人造物体的双向反射功能不均匀的影响；以及在不同的倾斜范围内进行测量时像素大小的变化。

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