Bilinear normal mixing model for spectral unmixing

Luo, Wenfei; Gao, Lianru; Zhang, Ruihao; Marinoni, Andrea; Zhang, Bing

doi:10.1049/iet-ipr.2018.5458

Cited by 17 publications

(9 citation statements)

References 78 publications

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“…For instance, the NCM has been considered to represent the uncertainties in EM estimation instead of the intrinsic variability of the material classes, which changes the problem by introducing statistical dependence between the different image pixels [226]. Other works also applied the NCM to problems such as nonlinear SU with a bilinear mixing model [227], for the linear unmixing of sediment grain size distribution (where the EMs represent the grain sizes of constituent materials) to study transport and deposition of sediments [228], or to represent the variability of the endmembers across multiple images in multitemporal SU, using additional spatially sparse terms accounting for potential abrupt spectral changes between the different images [229].…”

Section: Bayesian Methodsmentioning

confidence: 99%

Spectral Variability in Hyperspectral Data Unmixing: A comprehensive review

Borsoi

Imbiriba

Bermudez

et al. 2021

IEEE Geosci. Remote Sens. Mag.

145

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The spectral signatures of the materials contained in hyperspectral images (HI), also called endmembers (EM), can be significantly affected by variations in atmospheric, illumination or environmental conditions typically occurring within an HI. Traditional spectral unmixing (SU) algorithms neglect the spectral variability of the endmembers, what propagates significant mismodeling errors throughout the whole unmixing process and compromises the quality of its results. Therefore, large efforts have been recently dedicated to mitigate the effects of spectral variability in SU. This resulted in the development of algorithms that incorporate different strategies to allow the EMs to vary within an HI, using, for instance, sets of spectral signatures known a priori, Bayesian, parametric, or local EM models. Each of these approaches has different characteristics and underlying motivations. This paper presents a comprehensive literature review contextualizing both classic and recent approaches to solve this problem. We give a detailed evaluation of the sources of spectral variability and their effect in HI spectra. Furthermore, we propose a new taxonomy that organizes existing work according to a practitioner's point of view, based on the necessary amount of supervision and on the computational cost they require. We also review methods used to construct spectral libraries (which are required by many SU techniques) based on the observed HI, as well as algorithms for library augmentation and reduction. Finally, we conclude the paper with some discussions and an outline of possible future directions for the field.

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Section: Bayesian Methodsmentioning

confidence: 99%

Spectral Variability in Hyperspectral Data Unmixing: A comprehensive review

Borsoi

Imbiriba

Bermudez

et al. 2021

IEEE Geosci. Remote Sens. Mag.

145

View full text Add to dashboard Cite

show abstract

“…In [27], [28], bilinear models were extended with a scaling term to tackle external spectral variability. In [29], endmembers were modeled by a normal distribution to reduce the influence of endmember variability in bilinear models. In [30], a multitype mixing model was proposed to handle nonlinear unmixing and spectral variability for the purpose of HSI reconstruction.…”

Section: Introductionmentioning

confidence: 99%

Robust Supervised Method for Nonlinear Spectral Unmixing Accounting for Endmember Variability

Koirala

Zahiri

Lamberti

et al. 2021

IEEE Trans. Geosci. Remote Sensing

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Due to the complex interaction of light with mixed materials, reflectance spectra are highly nonlinear mixtures of the reflectances of the pure materials that are contained within a mixed pixel, making it hard to estimate the fractional abundances of the materials. Changing illumination conditions and crosssensor situations cause endmember variability, further complicating the unmixing. In this work, we propose a supervised approach to unmix mineral powder mixtures, containing endmember variability. The method combines a supervised regression method to learn the nonlinearity with a geodesic distance-based approach, that is invariant to endmember variability. Experiments are conducted on simulated and real mineral mixtures. In particular, we developed datasets of homogeneously mixed mineral powder mixtures, acquired by 2 different sensors, an Agrispec spectrometer and a snapscan shortwave infrared hyperspectral camera, under strictly controlled experimental settings. The proposed approach is compared to other supervised approaches and nonlinear mixture models.

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“…Undoubtedly, the high diversity and volume of hyperspectral data put us into a dilemma when we process and storage the data of large-scale HSI data. The bottleneck may appear in processing hyperspectral remote sensing data (e.g., classification [16] and unmixing [17]) in traditional single-server environment. In recent years, great efforts have been made towards the research on high-performance computing (HPC) of specialized hardware devices (e.g., field-programmable gate arrays (FPGAs), Beowulf clusters and distributed computers, multicore central processing units (CPUs), and graphics processing units (GPUs)) in hyperspectral applications [18].…”

Section: Introductionmentioning

confidence: 99%

A Parallel Unmixing-Based Content Retrieval System for Distributed Hyperspectral Imagery Repository on Cloud Computing Platforms

Zheng

Sun

et al. 2021

Remote Sensing

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As the volume of remotely sensed data grows significantly, content-based image retrieval (CBIR) becomes increasingly important, especially for cloud computing platforms that facilitate processing and storing big data in a parallel and distributed way. This paper proposes a novel parallel CBIR system for hyperspectral image (HSI) repository on cloud computing platforms under the guide of unmixed spectral information, i.e., endmembers and their associated fractional abundances, to retrieve hyperspectral scenes. However, existing unmixing methods would suffer extremely high computational burden when extracting meta-data from large-scale HSI data. To address this limitation, we implement a distributed and parallel unmixing method that operates on cloud computing platforms in parallel for accelerating the unmixing processing flow. In addition, we implement a global standard distributed HSI repository equipped with a large spectral library in a software-as-a-service mode, providing users with HSI storage, management, and retrieval services through web interfaces. Furthermore, the parallel implementation of unmixing processing is incorporated into the CBIR system to establish the parallel unmixing-based content retrieval system. The performance of our proposed parallel CBIR system was verified in terms of both unmixing efficiency and accuracy.

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Bilinear normal mixing model for spectral unmixing

Cited by 17 publications

References 78 publications

Spectral Variability in Hyperspectral Data Unmixing: A comprehensive review

Spectral Variability in Hyperspectral Data Unmixing: A comprehensive review

Robust Supervised Method for Nonlinear Spectral Unmixing Accounting for Endmember Variability

A Parallel Unmixing-Based Content Retrieval System for Distributed Hyperspectral Imagery Repository on Cloud Computing Platforms

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