A Novel Negative Abundance‐Oriented Hyperspectral Unmixing Algorithm

Marrero, Ruben; López, S.; Callicó, Gustavo M.; Veganzones, Miguel Ángel; Plaza, Antonio; Chanussot, Jocelyn; Sarmiento, Roberto

doi:10.1109/tgrs.2014.2383440

Cited by 26 publications

(9 citation statements)

References 34 publications

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“…Three non eigen-based hyperspectral ID estimators have recently been proposed. The first one, introduced in [34] as part of a Negative ABundance-Oriented (NABO) unmixing algorithm, borrows the main idea from the HySIME algorithm. Basically, it decomposes the residual error from the unconstrained unmixing into two components, a first due to noise and a second due to ID.…”

Section: A Related Workmentioning

confidence: 99%

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Hyperspectral Local Intrinsic Dimensionality

Drumetz

Veganzones

Gomez

et al. 2016

IEEE Trans. Geosci. Remote Sensing

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International audienceThe Intrinsic Dimensionality (ID) of multivariate data is a very important concept in spectral unmixing of hyperspectral images. A good estimation of the ID is crucial for a correct retrieval of the number of endmembers (the spectral signatures of macroscopic materials) in the image, for dimensionality reduction or for subspace learning, among others. Recently, some approaches to perform spectral unmixing and super-resolution locally have been proposed, which require a local estimation of the number of endmembers to use. However, the role of ID in local regions of hyperspectral images has not been properly addressed. Some important issues when dealing with small regions of hyperspectral data can seriously affect the performance of conventional hyperspectral ID estimators. We show that three factors mainly affect local ID estimation: the number of pixels in the local regions, which has to be high enough for the estimations to be relevant, the number of hyperspectral bands which complicates the estimations if the ambient space has a high dimensionality, and the noise, which can be misinterpreted as signal when its power is important. Here, we review the hyperspectral ID estimators on the literature for local ID estimation, we show how they behave in a local setting on synthetic and real datasets, and we provide some guidelines to make proper use of these estimators in local approaches

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Section: A Related Workmentioning

confidence: 99%

“…This technique [34] performs spectral unmixing and dimensionality estimation at the same time. It is noteworthy that this method is not eigenvalue-based.…”

Section: F Vertex Component Analysis/negative Abundance Oriented Algmentioning

confidence: 99%

Hyperspectral Local Intrinsic Dimensionality

Drumetz

Veganzones

Gomez

et al. 2016

IEEE Trans. Geosci. Remote Sensing

Self Cite

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“…where Y = [y 1 , y 2 , ..., y N ] is the hyperspectral image matrix, A = [α 1 , α 2 , ..., α N ] and N = [n 1 , n 2 , ..., n N ] represent the abundance matrix and the noise matrix, respectively. Due to physical limitations and constraints, the abundance needs to satisfy two constraints, namely the abundance sum-to-one constraint (ASC, ∑ m j=1 α j = 1) and the abundance nonnegative constraint (ANC, α j ≥ 0, j = 1, 2, ..., m) [10].…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Surrogate-Assisted Endmember Extraction Framework Based on Intelligent Optimization Algorithms for Hyperspectral Remote Sensing Images

Wang

Liu

et al. 2022

Remote Sensing

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As the foremost step of spectral unmixing, endmember extraction has been one of the most challenging techniques in the spectral unmixing processing due to the mixing of pixels and the complexity of hyperspectral remote sensing images. The existing geometrial-based endmember extraction algorithms have achieved the ideal results, but most of these algorithms perform poorly when they do not meet the assumption of simplex structure. Recently, many intelligent optimization algorithms have been employed to solve the problem of endmember extraction. Although they achieved the better performance than the geometrial-based algorithms in different complex scenarios, they also suffer from the time-consuming problem. In order to alleviate the above problems, balance the two key indicators of accuracy and running time, an adaptive surrogate-assisted endmember extraction (ASAEE) framework based on intelligent optimization algorithms is proposed for hyperspectral remote sensing images in this paper. In the proposed framework, the surrogate-assisted model is established to reduce the expensive time cost of the intelligent algorithms by fitting the fully constrained evaluation value with the low-cost estimated value. In more detail, three commonly used intelligent algorithms, namely genetic algorithm, particle swarm optimization algorithm and differential evolution algorithm, are specifically designed into the ASAEE framework to verify the effectiveness and robustness. In addition, an adaptive weight surrogate-assisted model selection strategy is proposed, which can automatically adjust the weights of different surrogate models according to the characteristics of different intelligent algorithms. Experimental results on three data sets (including two simulated data sets and one real data set) show the effectiveness and the excellent performance of the proposed ASAEE framework.

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“…And the analysis of the influence of the ambient temperature on the spectrum of the space target has not been carried out. 6,7 Additional scattering correction method 8 , mean variance correction 9 and normalization 10 are often used to analyze the characteristics of visible light. Compared with the above methods, the nonlinear temperature correction has a unique advantage in the correction of the model.…”

Section: Introductionmentioning

confidence: 99%

Nonlinear Temperature Correction Model of Spatial Material Spectrum Based on Taylor Expansion

Li¹,

Shi²,

Wu³

2018

dtetr

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According to the characteristics of temperature change in the complex space environment 1 , this paper proposes a nonlinear temperature based on Taylor expansion type correction model through the analysis of the spectral properties of several commonly used materials in space. In this model, a small number of sample prior information is used to establish the temperature difference function of the spectrum under different temperature and standard temperature. According to the temperature difference function matrix, the least squares method is used to fit the parameters of the curve, and the spline difference method is applied in the algorithm. The experimental results show that the algorithm has wide applicability in multi bands. The established calibration model can predict the material spectrum at a certain temperature. It has good practical application value.

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A Novel Negative Abundance‐Oriented Hyperspectral Unmixing Algorithm

Cited by 26 publications

References 34 publications

Hyperspectral Local Intrinsic Dimensionality

Hyperspectral Local Intrinsic Dimensionality

An Adaptive Surrogate-Assisted Endmember Extraction Framework Based on Intelligent Optimization Algorithms for Hyperspectral Remote Sensing Images

Nonlinear Temperature Correction Model of Spatial Material Spectrum Based on Taylor Expansion

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