This paper presents a unique multidimensional image processing approach for autonomous detection of anomalous materials in unknown natural clutter scenarios. Scene anomaly detection has a wide range of use in remote sensing applications requiring no specific material signatures. The approach uses a repeated multisampling scheme to characterize the unknown clutter background and the most popular anomaly detection algorithm - the Reed-Xiaoli algorithm - for scoring. The approach requires only a small fraction of the data cube to characterize clutter, it does not perform segmentation, and it is invariant to objects' scales (i.e., relative spatial sizes of objects in the imagery). Results using real multivariate spectral data are promising for autonomous manmade object detection tasks under different atmospheric conditions.