A Quantitative and Comparative Analysis of Endmember Extraction Algorithms From Hyperspectral Data

Martı́nez, Pablo; Pérez, R.; Plaza, Javier

doi:10.1109/tgrs.2003.820314

Cited by 564 publications

(315 citation statements)

References 21 publications

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“…The endmembers were extracted from the image using the purity pixel index method (PPI) (Rogge et al 2007;Plaza et al 2004). Due to the single vegetation cover, the problem of mixed pixels was not significant.…”

Section: Eo-1 Hyperion Remote-sensing Image Data Processingmentioning

confidence: 99%

Multiscale remote-sensing retrieval in the evapotranspiration of Haloxylon ammodendron in the Gurbantunggut desert, China

et al. 2012

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Transpiration, an essential component of surface evapotranspiration, is particularly important in the research of surface evapotranspiration in arid areas. The paper explores the spectral information of the arid vegetal evapotranspiration from a semi-empirical perspective by the measured data and the up-scaling method. The paper inverted the transpiration of Haloxylon ammodendron at the canopy, pixel and regional scales in the southern edge of the Gurbantunggut desert in Xinjiang, China. The results are as follows: at the canopy scale, the optimal exponential model of the sap flow rate based on the hyperspectrum is y = 0.0015e 3.8922x , R 2 = 0.806. At the pixel scale, there was a good linear relationship between the sap flow and the SR index, with a relationship of y = -1197.38x 3 ? 1048.43x 2 -305.47x ? 455.15, R 2 = 0.845. At the regional scale, based on the optimal exponential model and the EO-1 Hyperion remote-sensing data, the transpiration of the study area was inverted. Comparing the results of the SEBAL and SEBS models, the errors of the simulation results were 7.78 and 8.80 %. The paper made full use of the knowledge flow at different scales, bridging the scale difference in canopy and remote-sensing images to avoid the information bottleneck in the up-scaling. However, there are many constraints in the data acquirement, the efficiency of the models, the endmembers determination, the temporal-spatial up-scaling, and the accuracy assessment, which would be improved in future studies.

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Section: Eo-1 Hyperion Remote-sensing Image Data Processingmentioning

confidence: 99%

Multiscale remote-sensing retrieval in the evapotranspiration of Haloxylon ammodendron in the Gurbantunggut desert, China

et al. 2012

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“…Other endmember extraction algorithms and spectral distance measurements could have been used, possibly leading to different substances. A survey on EEA algorithms is conducted in [44].…”

Section: ) Endmembers Extractionmentioning

confidence: 99%

Fast Constrained Least Squares Spectral Unmixing Using Primal-Dual Interior-Point Optimization

Chouzenoux

Legendre

Moussaoui

et al. 2014

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…This is the central point of sparse unmixing techniques: they enforce the sparsity of the solution explicitly, as opposed to non-sparse techniques which aim at finding the correct set of endmembers from the spectral library but do not enforce sparseness explicitly. Examples of non-sparse techniques can be found in [4].…”

Section: The Sparse Unmixing Approachmentioning

confidence: 99%

“…To deal with the mixture problem, linear spectral mixture analysis techniques first identify a collection of spectrally pure constituent spectra, called endmembers in the literature [4], and then express the measured spectrum of each mixed pixel as a linear combination of endmembers weighted by fractions or abundances that indicate the proportion of each endmember present in the pixel. It should be noted that the linear mixture model assumes minimal secondary reflections and/or multiple scattering effects in the data collection procedure, and hence the measured spectra can be expressed as a linear combination of the spectral signatures of materials present in the mixed pixel [3].…”

Section: Introductionmentioning

confidence: 99%

Recent developments in sparse hyperspectral unmixing

Iordache

Bioucas-Dias

2010

2010 IEEE International Geoscience and Remote Sensing Symposium

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This paper explores the applicability of new sparse algorithms to perform spectral unmixing of hyperspectral images using available spectral libraries instead of resorting to well-known endmember extraction techniques widely available in the literature. Our main assumption is that it is unlikely to find pure pixels in real hyperspectral images due to available spatial resolution and mixing phenomena happening at different scales. The algorithms analyzed in our study rely on different principles, and their performance is quantitatively assessed using both simulated and real hyperspectral data sets. The experimental validation of sparse techniques conducted in this work indicates promising results of this new approach to attack the spectral unmixing problem in remotely sensed hyperspectral images.

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A Quantitative and Comparative Analysis of Endmember Extraction Algorithms From Hyperspectral Data

Cited by 564 publications

References 21 publications

Multiscale remote-sensing retrieval in the evapotranspiration of Haloxylon ammodendron in the Gurbantunggut desert, China

Multiscale remote-sensing retrieval in the evapotranspiration of Haloxylon ammodendron in the Gurbantunggut desert, China

Fast Constrained Least Squares Spectral Unmixing Using Primal-Dual Interior-Point Optimization

Recent developments in sparse hyperspectral unmixing

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