We propose a new method for autonomous manmade object detection, which is solely based on the use of second order statistics from two polarization components (0 and 90 deg) of polarimetric imagery. Using the approach, manmade objects can be detected as anomalies in scenes spatially dominated by natural objects. The approach exploits a key discovery: manmade objects are separable from natural objects in the (0 and 90 deg) variance-covariance space, holding invariant to diurnal cycle variation and geometry of illumination. Testing real imagery acquired outdoor (0.55 km sensor-to-target range) showed that the approach significantly outperforms the classical use of Stokes vector and DOLP (degree of linear polarization) during a full diurnal cycle.