Principal Component Analysis Scale-Invariant Feature Transform (PCA-SIFT) for Shared Principal Components

In the field of radar image processing, Principal Component Analysis Scale-Invariant Feature Transform (PCA-SIFT) serves as a widely-used algorithm for Synthetic Aperture Radar (SAR) image registration. This algorithm represents an enhanced version of the standard SIFT approach, implementing principal component analysis to achieve scale-invariant feature transformation for robust image alignment. The core implementation involves key stages: keypoint detection using difference-of-Gaussians, orientation assignment, and descriptor generation through PCA dimensionality reduction of gradient patches. Our provided code includes optimized functions for feature extraction, descriptor matching using Euclidean distance metrics, and transformation estimation for precise image registration. This implementation aims to assist radar enthusiasts and SAR professionals by improving both efficiency and accuracy in image alignment tasks through computationally efficient PCA-based descriptors that maintain rotational and scale invariance while reducing feature dimensionality.