Signature Evolution With Covariance Equalization In Oblique Hyperspectral Imagery

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 bidirection reflectance function of most man-made objects; and the change in pixel size when measurements are taken at different slant ranges.