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Image registration is used by the remote sensing community to align images for the purposes of examining changes in a scene. The application in this paper involves finding anomalies associated with human activity for the purpose of detecting underground nuclear explosions. This paper presents a non-rigid image registration algorithm that can be easily implemented using publicly available tools such as python, numpy, scipy, openCV and SIFT. SIFT is used to find feature correspondences between images. An approach based on Mahalanobis distance is used find a subset of robust correspondences. Comparisons are made to the RANSAC algorithm. The imagery was collected by DigitalGlobe’s Worldview-II satellite. One image pair is orthorectified. A second image pair is only geo-registered. Both image pairs were collected over mountainous desert regions, the second image pair has much rougher terrain and presents a challenging situation. The non-rigid property of the image registration algorithm allows for robust registration in mountainous terrain under different viewing geometries. Image differencing of the PAN-chromatic band is used to find changes, some of which are shown in detail for both sets of images. Overall registration improvement is quantified by using the standard deviation of the difference image. The non-rigid warping map was also applied to the multispectral bands of the DigitalGlobe data. This dataset made use of a multivariate change detection algorithm that incorporates the spectral properties of each pixel.