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.

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References

ADAMS, J. B. (1974). Visible and near-infrared diffuse reflectance: Spectra of pyroxenes as applied to remote sensing of solid objects in the solar system. J. Geophys. Res., 79, 4829 - 4836.

ADAMS, J. B., ADAMS J. (1984). Geologic mapping using Landsat MSS and TM images: Removing vegetation by modeling spectral mixtures. In: Thematic Conf. Remote Sens. for Expl. Geol. ERIM, 3, Proceedings, 2:615-622.

ADAMS, J. B., SMITH, M. O., JOHNSON, P. E. (1986). Spectral Mixture Modeling. A new analysis of rock and soil types at the Viking Lander 1 Side. J. Geophys. Res., 91:8-98-8122.

BAUGH, W. M., KRUSE, F. A., ATKINSON JR., W. W. (1998). Quantitative geochemical mapping of ammonium minerals in the southern cedar mountains, Nevada, using the airborne visible/infrared imaging spectrometer (AVIRIS). Remote Sens. Environ. 65:292-308.

BELL, J. F., HAWKE, B. R. (1995). Composional variability of the Serenitatis/Tranquillitatis region of the moon from telescopic multispectral imaging and spectroscopy, Icarus, 118:51-68.

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Published

2022-01-21

How to Cite

Abílio de Carvalho Júnior, O., Ferreira de Carvalho, A. P. ., Fontes Guimarães, R., Meneses, P. R., & Shimabukuro, Y. E. (2022). SPECTRICAL MIXTURE: (III) QUANTIFICATION. Space and Geography Journal, 6(1), 199–223. https://doi.org/10.26512/2236-56562003e39765

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.