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

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

Dora B. Heras¹  Alvaro Ordonez¹  Alvaro Accion¹  Francisco Arguello² 

[1] Centro Singular de Investigación en Tecnoloxías Intelixentes (CiTIUS), Universidade de Santiago de Compostela, Santiago de Compostela, Spain;Departamento de Electrónica e Computación, Universidade de Santiago de Compostela, Santiago de Compostela, Spain;

关键词: Hyperspectral imaging;    image registration;    maximally stable extremal regions (MSER);    remote sensing;    scale-invariant feature transform (SIFT);   

DOI  :  10.1109/JSTARS.2021.3129099

来源: DOAJ

【 摘 要 】

Image alignment is an essential task in many applications of hyperspectral remote sensing images. Before any processing, the images must be registered. Maximally stable extremal regions (MSER) is a feature detection algorithm that 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.

【 授权许可】

Unknown