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Si us plau utilitzeu sempre aquest identificador per citar o enllaçar aquest document: https://hdl.handle.net/2445/211320
Development and application of new strategies for data fusion of hyperspectral images
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[eng] Hyperspectral images (HSIs) are unique analytical measurements that provide spatial and chemical information about samples. Each pixel of a HSI contains a spectroscopic measurement, representing the chemical information of the material present at that specific area. Nowadays, there are extremely diverse hyperspectral imaging platforms. For example, offering HSIs with different spatial resolutions, such as the spatial resolution of fluorescence and infrared images, where the spatial resolution can vary from few nanometres to tens of microns, respectively or, on the other hand, providing different spectroscopic modalities, such as the excitation-emission HSIs, where each pixel is associated with a 2D excitation-emission landscape.
While the analysis of individual HSIs by chemometric methods provides comprehensive and rich chemical information about the nature of samples, often the connection and complementary information among the individual images remains unused and hidden. The integration and the simultaneous analysis of multiple HSIs in a single data structure or multiset, commonly referred as image fusion, offers a unique multiscale perspective of the sample constituents.
However, there are scenarios where the data fusion of hyperspectral images presents significant challenges, particularly when dealing with differences in scanned areas, spatial resolution, or spectral dimensionality. Moreover, there is a special interest in enhancing the analysis of fluorescence images due to their unique properties, especially when incorporating them into multiset structures. This integration offers distinct advantages in the field of image fusion.
This thesis proposes, on one hand, novel algorithms to enhance the analysis of excitation-emission fluorescence images and Time-resolved Fluorescence Spectroscopic data. These algorithms improve unmixing processes and facilitate the extraction of crucial information from fluorescence signals.
On the other hand, the thesis provides an open-access protocol for multiplatform image fusion, addressing differences in spectral dimensionality and coping with missing blocks of information. This involves developing flexible algorithms to handle varying spatial resolutions, scanned sample areas, and spectroscopic natures across different hyperspectral images.
The proposed algorithms and methodologies offer a significant advancement in the field of hyperspectral imaging analysis, enabling more comprehensive and insightful understanding of samples across various scales.
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GÓMEZ SÁNCHEZ, Adrián. Development and application of new strategies for data fusion of hyperspectral images. [consulta: 5 de desembre de 2025]. [Disponible a: https://hdl.handle.net/2445/211320]