RT Dissertation/Thesis
T1 Efficient Registration of Multi and Hyperspectral Remote Sensing Images on GPU
A1 Ordóñez Iglesias, Álvaro
K1 image registration
K1 hyperspectral images
K1 multispectral images
K1 remote sensing
K1 CUDA
K1 GPU
K1 Fourier transform
AB The advances in sensor development in the last few years allow obtaining multi and hyperspectral images at low cost. A previous fundamental task in many applications is the registration of images of the same scene which have been taken at different times from different viewpoints and which, furthermore, present changes in objects, in illumination, etc. In this thesis, the problem of developing faster and more efficient automatic hyperspectral image registration was addressed. The focus was on designing and developing registration methods by producing good registration results in terms of accuracy and efficient computation in commodity hardware. A Fourier-based method and different feature-based methods were implemented to align hyperspectral remote sensing images with large and unknown initial transformations. To handle these extreme situations, the developed algorithms efficiently exploit the available spectral information and not only the spatial one as it is common in the literature. Furthermore, they are projected onto many-core GPUs enabling real-time applications even for large datasets.
YR 2021
FD 2021
LK http://hdl.handle.net/10347/27248
UL http://hdl.handle.net/10347/27248
LA eng
DS Minerva
RD 24 abr 2026