Estimating abundance fractions of materials in hyperspectral images by fitting a post-nonlinear mixing model

Chen, Jie; Richard, Cédric; Honeiné, Paul

doi:10.1109/whispers.2013.8080639

Cited by 13 publications

(12 citation statements)

References 9 publications

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“…In hyperspectral images, a pixel is usually a spectral mixture of several spectral signatures of pure materials, termed endmembers, due to limited spatial resolution of devices and diversity of materials [58]. Although nonlinear mixture models have begun to support novel applications [59]- [61], the linear mixture model is still widely used for determining and quantifying materials in sensed images due to its simpler physical interpretation. With the linear mixture model, pixels can be decomposed as linear combinations of constituent spectra, weighted by fractions of abundance.…”

Section: Distributed Unmixing Of Hyperspectral Datamentioning

confidence: 99%

Multitask Diffusion Adaptation Over Networks

Chen

Richard

Sayed

2014

IEEE Trans. Signal Process.

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241

263

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Adaptive networks are suitable for decentralized inference tasks, e.g., to monitor complex natural phenomena.Recent research works have intensively studied distributed optimization problems in the case where the nodes have to estimate a single optimum parameter vector collaboratively. However, there are many important applications that are multitask-oriented in the sense that there are multiple optimum parameter vectors to be inferred simultaneously, in a collaborative manner, over the area covered by the network. In this paper, we employ diffusion strategies to develop distributed algorithms that address multitask problems by minimizing an appropriate mean-square error criterion with 2 -regularization. The stability and convergence of the algorithm in the mean and in the mean-square sense is analyzed. Simulations are conducted to verify the theoretical findings, and to illustrate how the distributed strategy can be used in several useful applications related to spectral sensing, target localization, and hyperspectral data unmixing.

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Section: Distributed Unmixing Of Hyperspectral Datamentioning

confidence: 99%

Multitask Diffusion Adaptation Over Networks

Chen

Richard

Sayed

2014

IEEE Trans. Signal Process.

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241

263

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“…This model was proposed in [21]. In [22] [23], the term ψ nlin is also called the residual term and the associated algorithm is called the residual component analysis.…”

Section: Performance Evaluationmentioning

confidence: 99%

“…• Interpreting results with intuitive observation: When testing unmixing algorithms with real data, most of the existing works [12], [20], [21], [25]- [29] illustrate the results in a straightforward manner by showing the estimated abundance maps and making visually intuitive comparisons. Clearly, this qualitative method is not very helpful to understand the performance of these algorithms.…”

Section: B Evaluation Of the Unmixing Performancementioning

confidence: 99%

A Laboratory-Created Dataset With Ground Truth for Hyperspectral Unmixing Evaluation

Zhao

Chen

2019

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

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Spectral unmixing is an important and challenging problem in hyperspectral data processing. This topic has been extensively studied and a variety of unmixing algorithms have been proposed in the literature. However, the lack of publicly available dataset with ground-truth makes it difficult to evaluate and compare the performance of unmixing algorithms in a quantitative and objective manner. Most of the existing works rely on the use of numerical synthetic data and an intuitive inspection of the results of real data. To alleviate this dilemma, in this study, we design several experimental scenes in our laboratory, including printed checkerboards, mixed quartz sands, and reflection with a vertical board. A dataset is then created by imaging these scenes with the hyperspectral camera in our laboratory, providing 36 mixtures with more than 130, 000 pixels with 256 wavelength bands ranging from 400nm to 1000nm. The experimental settings are strictly controlled so that pure material spectral signatures and material compositions are known. To the best of our knowledge, this dataset is the first publicly available dataset created in a systematic manner with groundtruth for spectral unmixing. Some typical linear and nonlinear unmixing algorithms are also tested with this dataset and lead to meaningful results.

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“…Such sensitivity to outliers is due to the investigated ℓ 2 -norm as a cost function in the FCLS and SUnSAL algorithms, as well as all unmixing algorithms that explore least-squares solutions. It is worth noting that nonlinear unmixing algorithms also suffer from this drawback, including the kernel-based fully-constrained least-squares (KFCLS) [15], nonlinear fluctuation methods [7] and post-nonlinear methods [16].Information theoretic learning provides an elegant alternative to the conventional minimization of the ℓ 2 -norm in least-squares…”

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confidence: 99%

“…decrease σ = σ 0 /p, and go to line 2 13: else 14: increase σ = 1.2σ, and go to line 2 15: end if 16: end if 13 V. EXPERIMENTS WITH SYNTHETIC DATA In this section, the performance of the proposed fully-constrained (CUSAL-FC) and sparsity-promoting (CUSAL-SP) algorithms is evaluated on synthetic data. A comparative study is performed considering six state-of-the-art methods proposed for linear and nonlinear unmixing models.…”

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confidence: 99%

Correntropy Maximization via ADMM: Application to Robust Hyperspectral Unmixing

Zhu

Halimi

Honeiné

et al. 2017

IEEE Trans. Geosci. Remote Sensing

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In hyperspectral images, some spectral bands suffer from low signal-to-noise ratio due to noisy acquisition and atmospheric effects, thus requiring robust techniques for the unmixing problem. This paper presents a robust supervised spectral unmixing approach for hyperspectral images. The robustness is achieved by writing the unmixing problem as the maximization of the correntropy criterion subject to the most commonly used constraints. Two unmixing problems are derived: the first problem considers the fully-constrained unmixing, with both the non-negativity and sum-to-one constraints, while the second one deals with the non-negativity and the sparsity-promoting of the abundances. The corresponding optimization problems are solved efficiently using an alternating direction method of multipliers (ADMM) approach. Experiments on synthetic and real hyperspectral images validate the performance of the proposed algorithms for different scenarios, demonstrating that the correntropy-based unmixing is robust to outlier bands. Index TermsCorrentropy, maximum correntropy estimation, alternating direction method of multipliers, hyperspectral image, unmixing problem. F. Zhu is with the A. Halimi is with the School of Engineering and Physical Sciences, Heriot-Watt University, U.K. (a.halimi@hw.ac.uk) P. Honeine is with the LITIS lab, ) 2 I. INTRODUCTION SPectral unmixing is an essential issue in many disciplines, including signal and image processing, with a wide range of applications, such as classification, segmentation, material identification and target detection. Typically, a hyperspectral image corresponds to a scene taken at many continuous and narrow bands across a certain wavelength range; namely, each pixel is a spectrum. Assuming that each spectrum is a mixture of several pure materials, the unmixing problem consists in two tasks: (i) identifying these pure materials (the so-called endmembers); (ii) estimating their proportions (the so-called abundances) at each pixel [1]. In practice, these two steps can be performed either sequentially or simultaneously [2]. Wellknown endmember extraction algorithms include the pure-pixel-based ones, e.g., the vertex component analysis (VCA) [3] and the N-FINDR [4], as well as the minimum-volume-based ones, e.g., the minimum simplex analysis [5] and the minimum volume constrained nonnegative matrix factorization [6]. While the endmember extraction is relatively easy from geometry, the abundance estimation remains an open problem. Indeed, the abundances can be estimated using least-squares methods, geometric approaches [2], or by tackling recently-raised issues such as nonlinearity [7], [8]. In this paper, we consider the abundance estimation problem. The linear mixture model (LMM) is the most investigated over the past decades [6], [9], [10]. Its underlying premise is that each pixel/spectrum is a linear combination of the endmembers. To be physically interpretable, two constraints are often enforced in the estimation problem: the abundance non-negativity constraint (ANC) and the a...

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Estimating abundance fractions of materials in hyperspectral images by fitting a post-nonlinear mixing model

Cited by 13 publications

References 9 publications

Multitask Diffusion Adaptation Over Networks

Multitask Diffusion Adaptation Over Networks

A Laboratory-Created Dataset With Ground Truth for Hyperspectral Unmixing Evaluation

Correntropy Maximization via ADMM: Application to Robust Hyperspectral Unmixing

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