Estimating the Intrinsic Dimension of Hyperspectral Images Using a Noise-Whitened Eigengap Approach

IEEE Trans. on Image Process.

2016

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Hyperspectral unmixing is aimed at identifying the reference spectral signatures composing an hyperspectral image and their relative abundance fractions in each pixel. In practice, the identified signatures may vary spectrally from an image to another due to varying acquisition conditions, thus inducing possibly significant estimation errors. Against this background, hyperspectral unmixing of several images acquired over the same area is of considerable interest. Indeed, such an analysis enables the endmembers of the scene to be tracked and the corresponding endmember variability to be characterized. Sequential endmember estimation from a set of hyperspectral images is expected to provide improved performance when compared to methods analyzing the images independently.However, the significant size of hyperspectral data precludes the use of batch procedures to jointly estimate the mixture parameters of a sequence of hyperspectral images. Provided that each elementary component is present in at least one image of the sequence, we propose to perform an online hyperspectral unmixing accounting for temporal endmember variability. The online hyperspectral unmixing is formulated as a two-stage stochastic program, which can be solved using a stochastic approximation. The performance of the proposed method is evaluated on synthetic and real data.A comparison with independent unmixing algorithms finally illustrates the interest of the proposed strategy. Index TermsHyperspectral imagery, perturbed linear unmixing (PLMM), endmember temporal variability, two-stage stochastic program, stochastic approximation (SA).

Section: A Description Of the Datasetmentioning

confidence: 99%

Online Unmixing of Multitemporal Hyperspectral Images Accounting for Spectral Variability

IEEE Trans. on Image Process.

2016

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“…5. The images have been unmixed with R = 3 endmembers based on results obtained from prior studies conducted on these data [31], [50], and confirmed by the results of the noise-whitened eigengap algorithm (NWEGA) [51] reported in Table II. After removing the water absorption bands, 173 out of the 224 available spectral bands were finally exploited.…”

Section: Experiments With Real Datamentioning

confidence: 63%

Partially Asynchronous Distributed Unmixing of Hyperspectral Images

IEEE Trans. Geosci. Remote Sensing

2019

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So far, the problem of unmixing large or multitemporal hyperspectral datasets has been specifically addressed in the remote sensing literature only by a few dedicated strategies. Among them, some attempts have been made within a distributed estimation framework, in particular relying on the alternating direction method of multipliers (ADMM). In this paper, we propose to study the interest of a partially asynchronous distributed unmixing procedure based on a recently proposed asynchronous algorithm. Under standard assumptions, the proposed algorithm inherits its convergence properties from recent contributions in non-convex optimization, while allowing the problem of interest to be efficiently addressed. Comparisons with a distributed synchronous counterpart of the proposed unmixing procedure allow its interest to be assessed on synthetic and real data. Besides, thanks to its genericity and flexibility, the procedure investigated in this work can be implemented to address various matrix factorization problems.

“…We consider a real sequence of AVIRIS HS images acquired over the Lake Tahoe region (California, United States of America) between 2014 and 2015 1 . The scene of interest (100 × 100), composed of a lake and a nearby field, has been unmixed with R = 3 endmembers based on the results of the noise-whitened eigengap algorithm (NWEGA) [53] applied to each image of the series (see Table III). This choice is further supported by results obtained from a previous analysis conducted on the same dataset [54,Appendix E].…”

Section: A Description Of the Datasetmentioning

confidence: 99%

A Hierarchical Bayesian Model Accounting for Endmember Variability and Abrupt Spectral Changes to Unmix Multitemporal Hyperspectral Images

IEEE Trans. Comput. Imaging

2018

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Abstract-Hyperspectral unmixing is a blind source separation problem which consists in estimating the reference spectral signatures contained in a hyperspectral image, as well as their relative contribution to each pixel according to a given mixture model. In practice, the process is further complexified by the inherent spectral variability of the observed scene and the possible presence of outliers. More specifically, multi-temporal hyperspectral images, i.e., sequences of hyperspectral images acquired over the same area at different time instants, are likely to simultaneously exhibit moderate endmember variability and abrupt spectral changes either due to outliers or to significant time intervals between consecutive acquisitions. Unless properly accounted for, these two perturbations can significantly affect the unmixing process. In this context, we propose a new unmixing model for multitemporal hyperspectral images accounting for smooth temporal variations, construed as spectral variability, and abrupt spectral changes interpreted as outliers. The proposed hierarchical Bayesian model is inferred using a Markov chain Monte-Carlo (MCMC) method allowing the posterior of interest to be sampled and Bayesian estimators to be approximated. A comparison with unmixing techniques from the literature on synthetic and real data allows the interest of the proposed approach to be appreciated.Index Terms-Hyperspectral imagery, multitemporal images, endmember variability, Markov chain Monte-Carlo (MCMC) methods.