Robust Unmixing Algorithms for Hyperspectral Imagery

Halimi, Abderrahim; Altmann, Yoann; Buller, Gerald S.; McLaughlin, Steve; Oxford, William; Clarke, Damien; Piper, Jonathan

doi:10.1109/sspd.2016.7590611

Cited by 5 publications

(5 citation statements)

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“…A mixing model describes how the endmembers are combined to form the mixed spectrum as measured by the sensor [6]. Given the mixing model, SU then estimates the inverse of the formation process to infer the quantity of interest, specifically the endmembers, and abundance from the collected spectra [7,8]. This could be achieved through a radiative transfer model which accurately describes the light scattering by the materials in the observed scene by a sensor [6].…”

Section: Introductionmentioning

confidence: 99%

“…A model proposed by Hapke [6] describes the interactions suffered by light when it comes into contact with a surface composed of particles; they involve meaningful and interpretable quantities that have physical significance, however, these models require a nonlinear formulation which is complex and complicates the derivation of the unmixing strategies [7]. These methods account for the intimate mixture of materials, as covered by a scene, in a dataset [1,8]. Different nonlinear mixing models exist, some motivated by physical arguments such as bilinear models, while others exploit a more flexible nonlinear mathematical model to improve the performance of the unmixing method [7].…”

Section: Introductionmentioning

confidence: 99%

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Hybrid Spectral Unmixing: Using Artificial Neural Networks for Linear/Non-Linear Switching

et al. 2017

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Spectral unmixing is a key process in identifying spectral signature of materials and quantifying their spatial distribution over an image. The linear model is expected to provide acceptable results when two assumptions are satisfied: (1) The mixing process should occur at macroscopic level and (2) Photons must interact with single material before reaching the sensor. However, these assumptions do not always hold and more complex nonlinear models are required. This study proposes a new hybrid method for switching between linear and nonlinear spectral unmixing of hyperspectral data based on artificial neural networks. The neural networks was trained with parameters within a window of the pixel under consideration. These parameters are computed to represent the diversity of the neighboring pixels and are based on the Spectral Angular Distance, Covariance and a non linearity parameter. The endmembers were extracted using Vertex Component Analysis while the abundances were estimated using the method identified by the neural networks (Vertex Component Analysis, Fully Constraint Least Square Method, Polynomial Post Nonlinear Mixing Model or Generalized Bilinear Model). Results show that the hybrid method performs better than each of the individual techniques with high overall accuracy, while the abundance estimation error is significantly lower than that obtained using the individual methods. Experiments on both synthetic dataset and real hyperspectral images demonstrated that the proposed hybrid switch method is efficient for solving spectral unmixing of hyperspectral images as compared to individual algorithms.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Hybrid Spectral Unmixing: Using Artificial Neural Networks for Linear/Non-Linear Switching

et al. 2017

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“…One scene is composed of 400 × 200 pixels in radiance in the VNIR range with 140 bands, with a resolution of 25 × 25cm. It is assumed that the background is composed of 5 components, two kinds of soil, road, grass and tree, whose spectra have been manually extracted and also used in [1]. Thirty man-made targets, made of either green or grey ceramic, exist in the scene.…”

Section: Test On Real Datasetsmentioning

confidence: 99%

Anomaly Detection With High Resolution Hyperspectral Observations

Chenot

Yaghoobi

Davies

et al. 2018

2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP)

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Hyperspectral images enable the detection of targets due to the high spectral sampling. The latest generation of sensors also provides an unprecedented spatial resolution which is further exploited in this article to uncover hard to detect anomalies. In particular, we model and estimate the background building upon robust supervised linear unmixing. We benefit from the high resolution of the data to spatially constrain the background. This provides a novel framework for exploiting both the spectral and the energy variations created by the presence of unknown targets to detect them.Index Terms-Hyperspectral imaging, anomaly detection, linear mixture model.

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“…2) Mismodelling effects (ME) or outliers: In recent years, there has been considerable interest in robust hyperspectral unmixing to enable adaptation of the simple LMM to realistic scenes which often present outliers or other unknown effects [40]. This goal can be achieved May 3, 2019 DRAFT using different strategies such as adapting the optimization cost function [41] or changing the observation model by introducing a residual term that accounts for the mismodelling effects [21], [29], [32].…”

Section: Mixture Modelsmentioning

confidence: 99%

Fast Hyperspectral Unmixing in Presence of Nonlinearity or Mismodeling Effects

Halimi

Bioucas-Dias

Dobigeon

et al. 2017

IEEE Trans. Comput. Imaging

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This paper presents two novel hyperspectral mixture models and associated unmixing algorithms. The two models assume a linear mixing model corrupted by an additive term whose expression can be adapted to account for multiple scattering nonlinearities (NL), or mismodelling effects (ME). The NL model generalizes bilinear models by taking into account higher order interaction terms. The ME model accounts for different effects such as endmember variability or the presence of outliers. The abundance and residual parameters of these models are estimated by considering a convex formulation suitable for fast estimation algorithms. This formulation accounts for constraints such as the sum-to-one and non-negativity of the abundances, the non-negativity of the nonlinearity coefficients, the spectral smoothness of the ME terms and the spatial sparseness of the residuals. The resulting convex problem is solved using the alternating direction method of multipliers (ADMM) whose convergence is ensured theoretically. The proposed mixture models and their unmixing algorithms are validated on both synthetic and real images showing competitive results regarding the quality of the inference and the computational complexity when compared to the state-of-the-art algorithms.

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Robust Unmixing Algorithms for Hyperspectral Imagery

Cited by 5 publications

References 20 publications

Hybrid Spectral Unmixing: Using Artificial Neural Networks for Linear/Non-Linear Switching

Hybrid Spectral Unmixing: Using Artificial Neural Networks for Linear/Non-Linear Switching

Anomaly Detection With High Resolution Hyperspectral Observations

Fast Hyperspectral Unmixing in Presence of Nonlinearity or Mismodeling Effects

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