SPECTRICAL MIXTURE: (III) QUANTIFICATION

Authors

  • Osmar Abílio de Carvalho Júnior INPE - Instituto Nacional de Pesquisas Espaciais 12201-970 - São José dos Campos - SP, Brasil
  • Ana Paula Ferreira de Carvalho UnB - Universidade de Brasília - Departamento de Ecologia Campus Universitário Darcy Ribeiro, Asa Norte - 70910-900, Brasília, DF, Brasil
  • Renato Fontes Guimarães UnB - Universidade de Brasília - Departamento de Geografia Campus Universitário Darcy Ribeiro, Asa Norte, 70910-900, Brasília, DF, Brasil.
  • Paulo Roberto Meneses UnB - Universidade de Brasília - Departamento de Geologia Campus Universitário Darcy Ribeiro, Asa Norte, 70910-900, Brasília, DF, Brasil.
  • Yosio Edemir Shimabukuro INPE - Instituto Nacional de Pesquisas Espaciais 12201-970 - São José dos Campos - SP, Brasil

DOI:

https://doi.org/10.26512/2236-56562003e39765

Keywords:

spectral mixture, spectral classification, remote sensing

Abstract

The relative abundance of a material can be determined establishing a proportionality relationship between a characteristic of the form of the spectrum and its quantity. In the case of analysis of spectra or of hyperspectral images the studies are focused on the features of diagnostic absorption of the elements. The present work aims to present a revision about two main methods of digital image processing for spectral quantification: the linear regression and spectral band depth. In this work is described the linear regression method as well as the methods that utilize the multiple linear regression such as the Linear Spectral Mixing Analysis and the further procedures as the Multiple Endmember Spectral Mixture Analysis (MESMA) method. The characteristics of the depth of the absorption band are detailed highlighting its effects in the mixture analysis.

References

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Published

2022-01-21

Issue

V.6 n. 1 (2003): SENSORIAMENTO REMOTO

Section

Paper

License

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

How to Cite

SPECTRICAL MIXTURE: (III) QUANTIFICATION. (2022). Space and Geography Journal, 6(1), 199-223. https://doi.org/10.26512/2236-56562003e39765