Abstract
The main aspects related to the so-called spectral mixture under the perspective of orbital imagery carried out by Earth observation sensors are presented and contextualized.
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Adams, J. B., & Adams, J. D. (1984). Geologic mapping using Landsat MSS and TM images: Removing vegetation by modeling spectral mixtures – remote sensing for exploration geology. In Proceedings of the third thematic conference: International symposium on remote sensing of environment, Colorado (pp. 16–19). Colorado Springs.
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 site. Journal of Geophysical Research, 91(B8), 8098–8112.
Alcântara, E. H., Barbosa, C. C. F., Stech, J. L., Novo, E. M. M., & Shimabukuro, Y. E. (2009). Improving the spectral unmixing algorithm to map water turbidity distributions. Environmental Modelling & Software, 24, 1051–1061.
Atkinson, P., Cutler, M., & Lewis, H. (1997). Mapping subpixel proportional land cover with AVHRR imagery. International Journal of Remote Sensing, 18, 917–935.
Bastin, L. (1997). Comparison of fuzzy c-means classification, linear mixture modeling and MLC probabilities as tools for unmixing coarse pixels. International Journal of Remote Sensing, 18, 3629–3648.
Boardman, J. W.(1989). Inversion of imaging spectrometry data using singular value decomposition. In: Proceedings of the 12th Canadian symposium on remote sensing. (Vol. 4, pp. 2069–2072).
Detchmendy, D. M., & Pace, W. H. (1972). A model spectral signature variability for mixtures. In F. Shahrokhi (Ed.), Remote sensing of Earth resources (Vol. 1, pp. 596–620). Tullahoma: The University of Tennessee.
Foody, G. M., Lucas, R. M., Curran, P. J., & Honzak, M. (1997). Non-linear mixture modeling without end-members using an artificial neural network. International Journal of Remote Sensing, 18, 937–953.
García-Haro, F., Sommner, S., & Kemper, T. (2005). A new tool for variable multiple endmember spectral mixture analysis (VMESMA). International Journal of Remote Sensing, 26, 2135–2162.
Heimes, F. J. (1977). Effects of scene proportions on spectral reflectance in Lodgepole pine. Dissertation (Master of Science) – Colorado State University, Fort Collins.
Horwitz, H. M., Nalepka, R. F., Ryde, P. D., & Morgenstern, J. P. (1971). Estimating the proportions of objects within a single resolution element of a multispectral scanner. In Proceedings of the 7th international symposium on remote sensing of environment, May 7–21, 1971 (pp. 1307–1320). Ann Arbor: Willow Run Laboratories.
Novo, E. M. L. M., & Shimabukuro, Y. E. (1994). Spectral mixture analysis of inland tropical waters. International Journal of Remote Sensing, 15(6), 1354–1356.
Pace, W. H., & Detchmendy, D. M. (1973). A fast algorithm for the decomposing of multispectral data into mixtures. In F. Shahrokhi (Ed.), Remote sensing of Earth resources (Vol. 2, pp. 831–847). Tullahoma: The University of Tennessee.
Pearson, R. (1973). Remote multispectral sensing of biomass. Dissertation (Ph.D.) – Colorado State University, Fort Collins.
Piromal, R. A. S. (2006). Avaliação do modelo 5-scale para simular valores de reflectância de unidades de paisagem da Floresta Nacional do Tapajós. 151 f. Dissertação (Mestrado em Sensoriamento Remoto) – São José dos Campos, Inpe. INPE-14645-TDI/205.
Ranson, K. J. (1975). Computer assisted classification of mixtures with simulated spectral signatures. Dissertation (Master of Science) – Colorado State University, Fort Collins.
Roberts, D. A., Smith, M., & Adams, J. (1993). Green vegetation, nonphotosynthetic vegetation, and soils in AVIRIS data. Remote Sensing of Environment, 44, 255–269.
Rosin, P. (2001). Robust pixel unmixing. IEEE Transactions on Geoscience and Remote Sensing, 39, 1978–1983.
Shimabukuro, Y. E. (1987). Shade images derived from linear mixing models of multispectral measurements of forested areas. (274 p.) Thesis (Doctor of Phylosophy) – Colorado State University, Fort Collins.
Shimabukuro, Y. E., Batista, G. T., Mello, E. M. K., Moreira, J. C., & Duarte, V. (1998). Using shade fraction image segmentation to evaluate deforestation in Landsat Thematic Mapper images of the Amazon region. International Journal of Remote Sensing, 19(3), 535–541.
Singer, R. B., & Mccord, T. B. (1979). Mars: Large scale mixing of bright and dark materials and implications for analysis of spectral reflectance. In Proceedings of the 10th lunar and planetary science conference, Houston, Texas (pp. 1825–1848). Houston: Lunar and Planetary Institute.
Smith, M. O., Johnson, P. E., & Adams, J. B. (1985). Quantitative determination of mineral types and abundances from reflectance spectra using principal component analysis. Journal of Geophysical Research, 90(S02), 797–804.
Ustin, S. L., Adams, J. B., Elvidge, C. D., Rejmanek, M., Rock, B. N., Smith, M. O., Thomas, R. W., & Woodward, R. A. (1986). Thematic mapper studies of semiarid shrub communities. Bioscience, 36(7), 446–452.
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Shimabukuro, Y.E., Ponzoni, F.J. (2019). Background. In: Spectral Mixture for Remote Sensing. Springer Remote Sensing/Photogrammetry. Springer, Cham. https://doi.org/10.1007/978-3-030-02017-0_1
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DOI: https://doi.org/10.1007/978-3-030-02017-0_1
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