Image registration is a common operation in any type of image processing, specially in remote sensing images. Since the publication of the scale–invariant feature transform (SIFT) method, several algorithms based on feature detection have been proposed. In particular, KAZE builds the scale space using a nonlinear diffusion filter instead of Gaussian filters. Nonlinear diffusion filtering allows applying a controlled blur while the important structures of the image are preserved. Hyperspectral images contain a large amount of spatial and spectral information that can be used to perform a more accurate registration. This article presents HSI–KAZE, a method to register hyperspectral remote sensing images based on KAZE but considering the spectral information. The proposed method combines the information of a set of preselected bands, and it adapts the keypoint descriptor and the matching stage to take into account the spectral information. The method is adequate to register images in extreme situations in which the scale between them is very different. The effectiveness of the proposed algorithm has been tested on real images taken on different dates, and presenting different types of changes. The experimental results show that the method is robust achieving image registrations with scales of up to $24.0\times$ .

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.

Registration is a fundamental previous task in many applications of hyperspectrometry. Most of the algorithms developed are designed to work with RGB images and ignore the execution time. This paper presents a phase correlation algorithm on GPU to register two remote sensing hyperspectral images. The proposed algorithm is based on principal component analysis, multilayer fractional Fourier transform, combination of log-polar maps, and peak processing. It is fully developed in CUDA for NVIDIA GPUs. Different techniques such as the efficient use of the memory hierarchy, the use of CUDA libraries, and the maximization of the occupancy have been applied to reach the best performance on GPU. The algorithm is robust achieving speedups in GPU of up to ${\text{240}}.{\text{6}}\times$.

Hyperspectral images contain a great amount of information which can be used to more robustly register such images. In this article, we present a phase correlation method to register two hyperspectral images that takes into account their multiband structure. The proposed method is based on principal component analysis, the multilayer fractional Fourier transform, a combination of log-polar maps, and peak processing. The combination of maps is aimed at highlighting some peaks in the log-polar map using information from different bands. The method is robust and has been successfully tested for any rotation angle with commonly used hyperspectral scenes in remote sensing for scales of up to 7.5× and with pairs of hyperspectral images taken on different dates by the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) sensor for scales of up to 6.0×.

In this article, we present a desktop application for the analysis, reference data generation, registration, and supervised spatial-spectral classification of hyperspectral remote-sensing images through a simple and intuitive interface. Regarding the classification ability, the different classification schemes are implemented by using a chain structure as a base. It consists of five configurable stages that must be executed in a fixed order: preprocessing, spatial processing, pixel-wise classification, combination, and post-processing. The modular implementation makes its extension easy by adding new algorithms for each stage or new classification chains. The tool has been designed as a platform that is open to the incorporation of algorithms by the users interested in comparing classification schemes. As an example of use, a classification scheme based on the Quick Shift (QS) algorithm for segmentation and on Extreme Learning Machines (ELMs) or Support Vector Machines (SVMs) for classification is also proposed. The application is license-free, runs on the Linux operating system, and was developed in C language using the GTK library, as well as other free libraries to build the graphical user interfaces (GUIs).