Resampling Estimation based RPC Metadata Verification in Satellite Imagery

Recent advances in machine learning and computer vision have made it simple to manipulate a variety of images, including satellite images. Most of the commercially available satellite images go through the process of orthorectification to remove potential distortions due to terrain variations. This orthorectification process typically involves the use of rational polynomial coefficients (RPC) that geometrically remap the pixels in the original image to the rectified image. This paper proposes a new method to verify the authenticity of these orthorectified images with respect to the associated RPC metadata. The steps include calculating the Residual Discrete Fourier Transform (DFT) pattern from the image using a linear predictor based residual spectral analysis and comparing with expected residual DFT pattern using the RPC metadata associated with the image. If the metadata associated with an orthorectified image is the correct one, then both the DFT patterns should have high structural similarity. We use SSIM (Structural Similarity Index Metric) to quantify the similarity and thereby verify if the data has been tampered or not. Detailed experimental results are presented to demonstrate the high accuracy of the proposed method in detecting manipulations.