Multispectral Image Matching Using SIFT and SURF Algorithm: A Review

Abstract

SIFT and SURF image matching were used in many industries such as survey and mapping, geology, medical and automotive. Multispectral sensors offered today become new challenge for researchers to study the performances of SIFT and SURF algorithms on multispectral image. Basically, multispectral image consists of more than three bands. As a result, the differences between those bands leads to nonlinear intensity between images. Both algorithm detectors using ‘blob detector’ that extracting the feature points as a key point for image matching later on. Hence, the less visibility of the feature on the multispectral images was one of the issues need to be solved. Many researchers investigate and propose a new strategy to extract and match the feature point using SIFT and SURF on multispectral image. The image fusions, combinations of different descriptors and revised or alteration of the algorithm themselves were among the approached taken by researchers in order to achieved good results.

Side-Scan Sonar Image Matching Method Based on Topology Representation

“…Lozano-Vázquez et al (2022) [15] explored an array of image enhancement and feature extraction methods, with a focus on reliably extracting image features such as SIFT and SURF under low-light conditions. Finally, Shaharom et al (2023) [16] presented a comprehensive review of the application of the SIFT and SURF algorithms in multispectral image matching, delving into various research strategies aimed at augmenting the performance of these algorithms in processing multispectral images. It is evident that the concerted efforts employing different feature descriptors enable the extraction of image features to a considerable extent.…”