Prospective Comparison of SURF and Binary Keypoint Descriptors for Fast Hyperspectral Remote Sensing Registration

Abstract

Image registration is a crucial process that involves determining the geometric transformation required to align multiple images. It plays a vital role in various remote sensing image processing tasks that involve analyzing changes among images. To enable real-time response, it is essential to have computationally efficient registration algorithms, especially when dealing with large datasets as is the case of hyperspectral images. This article presents a comparative analysis of two descriptors used to characterize local features of images prior to their matching and registration. The objective is to analyze whether the LATCH binary keypoint descriptor, which produces compact descriptors, provides similar results to the gradient-based SURF descriptor in terms of execution time and registration precision. To obtain the best computational performance, multithreaded implementations using OpenMP have been proposed. LATCH has proven to be 7× faster and as reliable as SURF in terms of accuracy on scale differences of up to 1.2×.