Image Matching Based on Two-Column Histogram Hashing and Improved RANSAC

Abstract

To improve computational efficiency in synthetic aperture radar (SAR) image matching, a fast image matching method using a novel two-step searching strategy (coarse-to-fine) is proposed in this letter. This method is based on two-column histogram (TCH) hashing and improved random sample consensus (RANSAC). First, coarse matching is conducted using a novel TCH hashing, which is notable for its robustness and speed. Compared with the discrete cosine transform used in perceptual hashing, TCH describes SAR images more accurately and rapidly. Then, in the refining stage, key points are detected and described in the coarser scales using scale-invariant feature transform. The Euclidean distance strategy and the improved RANSAC based on prior energy function (P-RANSAC) are then employed to implement matching. On the basis of prior information, a model of energy function has been constructed to improve sampling strategy. Experimental results on various SAR images show that the proposed approach outperforms the state-of-the-art algorithms in SAR image matching.