Enhancing Chemical Image Analysis: Utilizing the Successive Projection Algorithm for Unmixing
Hyperspectral imaging (HSI) is a powerful, non-invasive analytical technique extensively utilized in chemistry as it simultaneously captures morphological and chemical information from samples across a broad spectrum of chemically informative wavelengths. In this context, morphological information r...
| Autores: | , , , , , |
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| Formato: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2025 |
| País: | España |
| Recursos: | Consejo Superior de Investigaciones Científicas (CSIC) |
| Repositorio: | DIGITAL.CSIC. Repositorio Institucional del CSIC |
| OAI Identifier: | oai:digital.csic.es:10261/387355 |
| Acesso em linha: | http://hdl.handle.net/10261/387355 https://api.elsevier.com/content/abstract/scopus_id/105002490815 |
| Access Level: | acceso abierto |
| Palavra-chave: | Hyperspectral imaging (HSI) http://metadata.un.org/sdg/3 Ensure healthy lives and promote well-being for all at all ages |
| Resumo: | Hyperspectral imaging (HSI) is a powerful, non-invasive analytical technique extensively utilized in chemistry as it simultaneously captures morphological and chemical information from samples across a broad spectrum of chemically informative wavelengths. In this context, morphological information refers to the spatial structure, shape, texture, and distribution of elements within the image. Enhancing its already widespread application requires reducing the computational load of the voluminous hyperspectral images while unmixing signals from different chemical species with unknown spectral fingerprints. Endmember extraction, which involves finding the purest spectral signatures within the data, is needed for decomposing these mixed signals. By resolving mixed pixels into their constituent endmembers, HSI enables accurate quantification and spatial mapping of chemical components, even when prior knowledge is limited. Current methods for endmember extraction, such as NFINDR, VCA, PPI, SIMPLISMA, and AMEE, are limited by issues including computational slowness, the requirement for extensive parameter optimization, and a lack of hierarchical consistency. Consequently, there is a pressing need for a method that is both faster and more accurate. Successive Projection Algorithm (SPA) is developed for forward wavelength selection to improve the predictive accuracy of regression models under strong collinearity. SPA emerges as a rapid and accurate endmember extraction technique, with applications extending beyond chemistry to areas such as food safety, environmental monitoring, and material analysis. Comparative analyses using both simulated and experimental datasets illustrate SPA's superior robustness, repeatability, absence of parameter tuning requirements, and computational efficiency when compared with the methods in current use. These findings show the value of SPA as a robust tool for computationally efficient hyperspectral image analysis in chemical applications and beyond. |
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