Experimental results related to the paper “HSI–MSER: Hyperspectral Image Registration Algorithm based on MSER and SIFT” by Álvaro Ordóñez, Álvaro Acción, Francisco Argüello, and Dora B. Heras, published in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

Image 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.

Compiled program to register two hyperspectral images.

• HSI-MSER algorithm: hsimser.zip

All images used in the paper are available in Registration Repository

Example of registration considered in this work: a) Reference image (size 470×900), b) Target image, and c) Result of the registration process showing the correctly registered superposition of the reference and target registered image (scale 7.5× and angle of rotation 3.5º).

Matched ellipses detected in band 8 of the Jasper Ridge images.

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