On the Convergence of N-FINDR and Related Algorithms: To Iterate or Not to Iterate?

Dowler, Shaun; Andrews, Mark

doi:10.1109/lgrs.2010.2049639

Cited by 17 publications

(9 citation statements)

References 19 publications

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“…The distinction between linear and nonlinear unmixing has been widely studied in recent years [11]. The exploitation of the more general linear mixture model, in spite of its simplicity, has fostered a large amount of research leading to a plethora of endmember identification algorithms based on geometrical and statistical approaches [12]- [37]. A recent trend is the design of algorithms that do not assume the presence of pure signatures in the input data [38]- [42].…”

mentioning

confidence: 99%

Total Variation Spatial Regularization for Sparse Hyperspectral Unmixing

Iordache

Bioucas-Dias

Plaza

2012

IEEE Trans. Geosci. Remote Sensing

677

478

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Abstract-Spectral unmixing aims at estimating the fractional abundances of pure spectral signatures (also called endmembers) in each mixed pixel collected by a remote sensing hyperspectral imaging instrument. In recent work, the linear spectral unmixing problem has been approached in semisupervised fashion as a sparse regression one, under the assumption that the observed image signatures can be expressed as linear combinations of pure spectra, known a priori and available in a library. It happens, however, that sparse unmixing focuses on analyzing the hyperspectral data without incorporating spatial information. In this paper, we include the total variation (TV) regularization to the classical sparse regression formulation, thus exploiting the spatialcontextual information present in the hyperspectral images and developing a new algorithm called sparse unmixing via variable splitting augmented Lagrangian and TV. Our experimental results, conducted with both simulated and real hyperspectral data sets, indicate the potential of including spatial information (through the TV term) on sparse unmixing formulations for improved characterization of mixed pixels in hyperspectral imagery.Index Terms-Hyperspectral imaging, sparse regression, sparse unmixing, spectral unmixing, total variation (TV) regularization.

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mentioning

confidence: 99%

Total Variation Spatial Regularization for Sparse Hyperspectral Unmixing

Iordache

Bioucas-Dias

Plaza

2012

IEEE Trans. Geosci. Remote Sensing

677

478

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“…Two widely used strategies were summarized in [26]. The first strategy is that the first replacement which results in larger volume would be made permanent.…”

Section: Replacement Strategiesmentioning

confidence: 99%

New Improvements in Parallel Implementation of N-FINDR Algorithm

Luo

Zhang

Jia

2012

IEEE Trans. Geosci. Remote Sensing

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Endmember extraction (EE) is the first step in hyperspectral data unmixing. N-FINDR is one of the most commonly used EE algorithms. Nevertheless, its computational complexity is high, particularly, for a large data set. Following a parallel version of N-FINDR, i.e., P-FINDR, further improvements are presented in this paper. First, generic endmember re-extraction operation (GERO) and multiple search paths are introduced such that multiple endmembers are extracted in parallel. Second, by making full use of the advantages of the proposed algorithms, two extended schemes, i.e., extended mapping rule and multiple-stage GERO are presented, which can reduce synchronous cost and provide steady parallel performance. In experiments, the proposed algorithms have been quantitatively evaluated. The results demonstrate that they can outperform the conventional parallel computing and do not degrade the quality of EE.Index Terms-Endmember extraction (EE), hyperspectral remote sensing, N-FINDR, parallel computing.

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“…Two commonly-used versions are used to implement N-FINDR. It was well discussed in [5,6]. The first implementation, called iterative N-FINDR (IN-FINDR), breaks up the exhaustive search procedure into two iterative loops.…”

Section: Parallel Implementation Parrerns Ofmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…Recently, many endmember extraction algorithms (EEAs) have been developed. N-FINDR [2] algorithm is one of the most commonly used EEA and different implementation had been explored [3][4][5][6].Nevertheless, endmember extraction in a large volume hyperspectral remote sensing image is a time-consuming task which cannot meet the time requirement of many applications. The utilization of the high performance computing (HPC) technique into hyperspectral remote sensing analysis throws a new light on the hyperspectral image processing algorithm research and becomes more and more widespread today [7].…”

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confidence: 99%

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Synchronous, asynchronous and grouping asynchronous parallel implementation for N-FINDR algorithms in hyperspectral remote sensing image

Luo

Zhang

2011

2011 IEEE International Geoscience and Remote Sensing Symposium

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N-FINDR is a widely used endmember extraction algorithm in hyperspectral imagery. Nevertheless, its computational complexity is high. Plaza's parallel implementation of N-FINDR, namely, P-FINDR, demonstrates an excellent way to improve the computing performance of N-FINDR by incorporating with parallel computing technique. In this paper, three parallel implementation patterns, i.e., synchronous, asynchronous and grouping asynchronous pattern, are presented. Two versions of N-FINDR, i.e. iterative N-FINDR and successive N-FINDR, are considered to be implemented in parallel by these patterns respectively. Thus obtains six parallel N-FINDR algorithms. In experiment, both solution quality and parallel performance of these algorithms are compared and suitable patterns for parallel implementation of N-FINDR are obtained. IndexTerms-hyperspectral remote sensing, endmember, N-FINDR, parallel computing INTRODUCTIONSpectral unmixing in hyperspectral remote sensing image has been widely researched in the last decade [1]. Endmember extraction is the most important and challenging task in spectral unmixing. Recently, many endmember extraction algorithms (EEAs) have been developed. N-FINDR [2] algorithm is one of the most commonly used EEA and different implementation had been explored [3][4][5][6].Nevertheless, endmember extraction in a large volume hyperspectral remote sensing image is a time-consuming task which cannot meet the time requirement of many applications. The utilization of the high performance computing (HPC) technique into hyperspectral remote sensing analysis throws a new light on the hyperspectral image processing algorithm research and becomes more and more widespread today [7]. Recently, many state-of-the-art parallel EEAs had been developed. A parallel version of N-FINDR was developed in [8], which demonstrated high computing performance and greatly improve the efficiency of N-FINDR algorithm.

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On the Convergence of N-FINDR and Related Algorithms: To Iterate or Not to Iterate?

Cited by 17 publications

References 19 publications

Total Variation Spatial Regularization for Sparse Hyperspectral Unmixing

Total Variation Spatial Regularization for Sparse Hyperspectral Unmixing

New Improvements in Parallel Implementation of N-FINDR Algorithm

Synchronous, asynchronous and grouping asynchronous parallel implementation for N-FINDR algorithms in hyperspectral remote sensing image

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