Accurate Registration Using Adaptive Block Processing for Multispectral Images

Image registration is a challenging task, with applications in surveillance, motion estimation, and fusion systems. Due to the diversity of sensors, local distortions and large image size, satellite images are often difficult to accurately register. In the literature, local descriptor-based processing techniques, such as scale-invariant feature transforms (SIAdaptive Block ProcessingFTs), have been applied to register satellite images, which provide robust features. However, these techniques suffer from a high-computational cost, lack of features, and low-distribution quality, which affect the registration accuracy. In this paper, we develop an algorithm to register satellite images based on adaptive block processing to increase the number of features and to improve the distribution quality. In addition, outlier removal using statistical masks are associated with classical random sample consensus (RANSAC); a subsequent comparative analysis demonstrates the accuracy of the proposed method. Typically, a classical SIFT prevents its wide application in recent remote sensing, although this is no longer the case with the proposed adaptive block processing method.