HSI-MSER: Hyperspectral Image Registration Algorithm based on MSER and SIFT

TítuloHSI-MSER: Hyperspectral Image Registration Algorithm based on MSER and SIFT
AutoresÁlvaro Ordóñez, Álvaro Acción, Francisco Argüello, and Dora B. Heras
TipoArtículo de revista
Fonte IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, IEEE, 2021.
RankProvisionally ranked Q1 in Computers in Earth Sciences by CiteScore 2020
DOI10.1109/JSTARS.2021.3129099
AbstractImage alignment is an essential task in many applications of hyperspectral remote sensing images. Before any processing, the images must be registered. The Maximally Stable Extremal Regions (MSER) is a feature detection algorithm which extracts regions by thresholding the image at different grey levels. These extremal regions are invariant to image transformations making them ideal for registration. The Scale-Invariant Feature Transform (SIFT) is a well-known keypoint detector and descriptor based on the construction of a Gaussian scale-space. This article presents a hyperspectral remote sensing image registration method based on MSER for feature detection and SIFT for feature description. It efficiently exploits the information contained in the different spectral bands to improve the image alignment. The experimental results over nine hyperspectral images show that the proposed method achieves a higher number of correct registration cases using less computational resources than other hyperspectral registration methods. Results are evaluated in terms of accuracy of the registration and also in terms of execution time.
Palabras chaveImage registration, Hyperspectral image, Image Feature Extraction