This paper presents an investigation of area-based image correlation under full perspective geometric distortion. Image matching techniques in the presence of geometric distortion modeled by affine transformations have been examined thoroughly in earlier work while this research investigates the effects of full-perspective distortion on spatial domain area-based matching techniques. With area- based techniques, there are two factors that heavily affect correlation accuracy; first, the amount of signal variation within the target window must be sufficient to provide detectable similarity of imagery and second, the amount of geometric distortion within the window must be small enough not to inhibit matching. Signal variation is increased by increasing the target window size while the effects of perspective geometric distortion are minimized through smaller target windows. Window dimensionality is appropriately adapted to accommodate these two conflicting effects. Window adaptation based on precomputed metrics is applied to extend distortion toleration. The image sets are derived from a tactical scenario and are geometrically transformed through planar assumptions by a nonaffine spatial mapping. The two images are then registered through different correlation techniques, the defining functions analyzed and limitations on the amount of perspective viewpoint change of an imaging system in an aerial tactical arena are given while still allowing proper image correspondence.