Rexistrado de imaxes hiperespectrais baseado na transformada rápida de Fourier

Traballo Fin de Grao en Enxeñaría Informática. Curso 2014-2015O proxecto centrarase no estudo de diferentes algoritmos xa existentes para rexistro de imaxes RGB que están baseados na transformada rápida de Fourier. A raíz dese estudo seleccionaranse os máis prometedores para desenvolvelos e analizar diferentes posibilidades de aplicación destes algoritmos ás imaxes hiperespectrais, pois a súa aplicación non é directa. Investigarase tamén sobre diferentes métodos de extracción de características das imaxes hiperespectrais para así poder reducir a súa dimensionalidade e quedarnos coa información máis relevante sobre a que se realizará o rexistrado. Os algoritmos desenvolvidos serán capaces de realizar o rexistrado de dúas imaxes hiperespectrais do mesmo obxecto ou localización, facendo corresponder os diferentes puntos mediante o cálculo do ángulo de rotación, factor de escala e translación dunha imaxe respecto da outra. Estes resultados deberán ser amosados tanto numéricamente como xerar a imaxe resultante do proceso. Traballarase sobre diferentes posibilidades de aplicación destas técnicas sobre diferentes versións do algoritmo. Realizaranse numerosas probas e modificacións co fin de conseguir un bo método de rexistrado. Concluírase o traballo cunha análise dos resultados obtidos

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

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 timeMinisterio de Ciencia e Innovación, Government of Spain PID2019-104834GB-I00; Consellería de Cultura, Educación e Universidade (Grant Number: ED431C 2018/19 and 2019-2022 ED431G-2019/04); Junta de Castilla y León under Project VA226P20; 10.13039/501100008530-European Regional Development Fund; Ministerio de Universidades, Government of Spain (Grant Number: FPU16/03537)S

GPU Accelerated FFT-Based Registration of Hyperspectral Scenes

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 240.6×This work was supported in part by the Consellería de Cultura, Educacion e Ordenación Universitaria under Grant GRC2014/008 and Grant ED431G/08 and in part by the Ministry of Education, Culture and Sport, Government of Spain under Grant TIN2013-41129-P and Grant TIN2016-76373-P. Both are cofunded by the European Regional Development Fund. The work of A. Ordóñez was supported by the Ministry of Education, Culture and Sport, Government of Spain, under an FPU Grant FPU16/03537S

Surf-Based Registration for Hyperspectral Images

The alignment of images, also known as registration, is a relevant task in the processing of hyperspectral images. Among the feature-based registration methods, Speeded Up Robust Features (SURF) has been proposed as a computationally efficient approach. In this paper HSI–SURF is proposed. This is a method to register hyperspectral remote sensing images based on SURF that takes advantage of the full spectral information of the images. In this sense, the proposed method selects specific bands of the images and adapts the keypoint descriptor and the matching stages to benefit from the spectral information, thus increasing the effectiveness of the registration.This work was supported in part by the Consellería de Educación, Universidade e Formación Profesional [grant numbers GRC2014/008, ED431C 2018/19, and ED431G/08] and Ministerio de Economía y Empresa, Government of Spain [grant number TIN2016-76373-P] and by Junta de Castilla y Leon - ERDF (PROPHET Project) [grant number VA082P17]. All are cofunded by the European Regional Development Fund (ERDF). The work of Alvaro Ordóñez was also supported by the Ministerio de Ciencia, Innovación y Universidades, Government of Spain, under a FPU Grant [grant number FPU16/03537

Fourier–Mellin registration of two hyperspectral images

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×This work was supported in part by the Consellería de Cultura, Educación e Ordenación Universitaria [grant numbers GRC2014/008 and ED431G/08] and Ministry of Education, Culture and Sport, Government of Spain [grant numbers TIN2013-41129-P and TIN2016-76373-P] both are co-funded by the European Regional Development Fund (ERDF)S

Comparing area–based and feature–based methods for co–registration of multispectral bands on GPU

This a post-print of the article “Comparing Area-Based and Feature-Based Methods for CoRegistration of Multispectral Bands on GPU” published in the Proceedings of IGARSS 2021 - 2021 IEEE International Geoscience and Remote Sensing SymposiumRegistration is required as a previous step for processing multispectral images. The different bands captured by each sensor for each image, as well as the different images corresponding to the same area, need to be aligned. In this paper, a 2– level registration scheme comparing the results obtained by the hyperspectral Fourier–Mellin (HYFM) and hyperspectral KAZE (HSI–KAZE) registration methods is proposed. It is designed for efficient implementation in a multi-GPU system in which different scenes are registered in parallel on different GPU

Exploring the Registration of Remote Sensing Images using HSI-KAZE in Graphical Units

Computational and Mathematical Methods in Science and Engineering (CMMSE), Rota, Cadiz, Spain, 30 June - 6 July 2019 (Session I, Part 5)Registration of hyperspectral remote sensing images is a common task in many image processing applications such as land use classification, environmental monitoring and change detection. The images to be registered present differences as a consequence of being obtained from different points of view, differences in the number of spectral bands captured by the sensors, in illumination and intensity, and also changes in the objects present in the images, among others. Feature-based methods as HSI-KAZE are more efficient at registering than area-based methods when the images are very rich in geometrical details, as it is the case for remote sensing images. But they present, nevertheless, the problem of being computationally more costly because the number of distinctive points to be calculated for these images is high. HSI-KAZE is a method to register hyperspectral remote sensing images based on KAZE features but considering the spectral information. In this work, a robust and efficient implementation of this method on programmable GPUs is presentedThis work was supported in part by the Consellería de Educación, Universidade e Formación Profesional [grant numbers GRC2014/008, ED431C 2018/19, and ED431G/08] and Ministerio de Economía y Empresa, Government of Spain [grant number TIN2016-76373-P] and by Junta de Castilla y Leon - ERDF (PROPHET Project) [grant number VA082P17]. All are co-funded by the European Regional Development Fund (ERDF). The work of Álvaro Ordóñez was also supported by Ministerio de Ciencia, Innovación y Universidades, Government of Spain, under a FPU Grant [grant numbers FPU16/03537 and EST18/00602

Alignment of Hyperspectral Images Using KAZE Features

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×This research was supported in part by the Consellería de Cultura, Educación e Ordenación Universitaria, Xunta de Galicia [grant numbers GRC2014/008 and ED431G/08] and Ministerio de Educación, Cultura y Deporte [grant number TIN2016-76373-P] both are co–funded by the European Regional Development Fund. The work of Álvaro Ordóñez was supported by the Ministerio de Educación, Cultura y Deporte under an FPU Grant [grant number FPU16/03537]. This work was also partially supported by Consejería de Educación, Junta de Castilla y León (PROPHET Project) [grant number VA082P17]S

A multi-device version of the HYFMGPU algorithm for hyperspectral scenes registration

This is a post-peer-review, pre-copyedit version of an article published in The Journal of Supercomputing. The final authenticated version is available online at: https://doi.org/10.1007/s11227-018-2689-7Hyperspectral image registration is a relevant task for real-time applications like environmental disasters management or search and rescue scenarios. Traditional algorithms were not really devoted to real-time performance, even when ported to GPUs or other parallel devices. Thus, the HYFMGPU algorithm arose as a solution to such a lack. Nevertheless, as sensors are expected to evolve and thus generate images with finer resolutions and wider wavelength ranges, a multi-GPU implementation of this algorithm seems to be necessary in a near future. This work presents a multi-device MPI + CUDA implementation of the HYFMGPU algorithm that distributes all its stages among several GPUs. This version has been validated testing it for 5 different real hyperspectral images, with sizes from about 80 MB to nearly 2 GB, achieving speedups for the whole execution of the algorithm from 1.18 × to 1.59 × in 2 GPUs and from 1.26 × to 2.58 × in 4 GPUs. The parallelization efficiencies obtained are stable around 86 % and 78 % for 2 and 4 GPUs, respectively, which proves the scalability of this multi-device versionThis work has been partially supported by: Universidad de Valladolid—Consejería de Educación of Junta de Castilla y León, Ministerio de Economía, Industria y Competitividad of Spain, and European Regional Development Fund (ERDF) program: Project PCAS (TIN2017-88614-R), Project PROPHET (VA082P17) and CAPAP-H6 network (TIN2016-81840-REDT). Universidade de Santiago de Compostela—Consellería de Cultura, Educación e Ordenación Universitaria of Xunta de Galicia (grant numbers GRC2014/008 and ED431G/08) and Ministerio de Economía, Industria y Competitividad of Spain (Grant Number TIN2016-76373-P), all co-funded by the European Regional Development Fund (ERDF) program. The work of Álvaro Ordóñez was supported by the Ministerio de Educación, Cultura y Deporte under an FPU Grant (Grant Number FPU16/03537)S

GPU Accelerated Registration of Hyperspectral Images Using KAZE Features

This is a post-peer-review, pre-copyedit version of an article published in The Journal of Supercomputing. The final authenticated version is available online at: https://doi.org/10.1007/s11227-020-03214-0Image registration is a common task in remote sensing, consisting in aligning different images of the same scene. It is a computationally expensive process, especially if high precision is required, the resolution is high, or consist of a large number of bands, as is the case of the hyperspectral images. HSIKAZEisaregistration method specially adapted for hyperspectral images that is based on feature detection and takes profit of the spatial and the spectral information available in those images. In this paper, an implementation of the HSI–KAZE registration algorithm on GPUs using CUDA is proposed. It detects keypoints based on non–linear diffusion filtering and is suitable for on–board processing of high resolution hyperspectral images. The algorithm includes a band selection method based on the entropy, construction of a scale-space through of non-linear filtering, keypoint detection with position refinement, and keypoint descriptors with spatial and spectral parts. Several techniques have been applied to obtain optimum performance on the GPUThis work was supported in part by the Consellería de Educación, Universidade e Formación Profesional [Grant Nos. GRC2014/008, ED431C 2018/19 and ED431G/08] and Ministerio de Economía y Empresa, Government of Spain [grant number TIN2016-76373-P] and by Junta de Castilla y Leon - ERDF (PROPHET Project) [Grant No. VA082P17]. All are cofunded by the European Regional Development Fund (ERDF). The work of Álvaro Ordóñez was also supported by Ministerio de Ciencia, Innovación y Universidades, Government of Spain, under a FPU Grant [Grant Nos. FPU16/03537 and EST18/00602]S