Algorithm taxonomy for hyperspectral unmixing

Keshava, Nirmal; Kerekes, John P.; Manolakis, Dimitris G.; Shaw, Gary A.

doi:10.1117/12.410362

Cited by 63 publications

(41 citation statements)

References 32 publications

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“…ICA has been used in a variety of applications for hyperspectral data and ICA-based DR has generally been found to be more effective than the other conventional variance-based techniques in the literature. On the contrary, there have been some studies revealing that its suitability to the problem characteristics has been questioned [18,19]. Our findings suggest that DR techniques may have a significant effect on the classification performance and can improve classification accuracies substantially.…”

Section: Resultsmentioning

confidence: 53%

A comparative analysis of classification methods for hyperspectral images generated with conventional dimension reduction methods

Arslan¹,

Akyürek²,

Kaya³

2017

Turk J Elec Eng & Comp Sci

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This paper compared performances of classification methods for a hyperspectral image dataset in view of dimensionality reduction (DR). Among conventional DR methods, principal component analysis, maximum noise fraction, and independent component analysis were used for the purpose of dimension reduction. The study was conducted using these DR techniques on a real hyperspectral image, an AVIRIS dataset with 224 bands, throughout the experiments. It was observed that DR may have a significant effect on the classification performance. After the DR methods were applied to the image dataset, the extracted reduced bands were used for testing classification performances. Four commonly used classification methods including maximum likelihood, Mahalanobis distance, spectral angle mapper, and support vector machines were used for the classification of dimensionally reduced and original (not reduced) images to test whether classification accuracies differed significantly for these images. Experiments reported in this study indicate that second-order statistics-based DR methods could efficiently reduce the dimensionality of the hyperspectral dataset.The maximum likelihood method generally performed better than the other classifiers while the spectral angle mapper exhibited significantly lower accuracies in the experiments. Inclusion of a texture measure in classification improved the classification results in a significant manner.

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Section: Resultsmentioning

confidence: 53%

A comparative analysis of classification methods for hyperspectral images generated with conventional dimension reduction methods

Arslan¹,

Akyürek²,

Kaya³

2017

Turk J Elec Eng & Comp Sci

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“…Abundance maps provide useful visualizations of hyperspectral data, showing where each endmember is located in an image and how completely each pixel is filled by that endmember. Depending on the algorithm and the application, endmembers may be determined first and subsequently utilized for abundance quantification, the endmembers and abundances may be found simultaneously, or abundances may be computed without any prior endmember information [7].…”

Section: Introductionmentioning

confidence: 99%

Using Physically-Modeled Synthetic Data to Assess Hyperspectral Unmixing Approaches

Stites¹,

Gunther

Moon

et al. 2013

Remote Sensing

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This paper considers an experimental approach for assessing algorithms used to exploit remotely sensed data. The approach employs synthetic images that are generated using physical models to make them more realistic while still providing ground truth data for quantitative evaluation. This approach complements the common approach of using real data and/or simple model-generated data. To demonstrate the value of such an approach, the behavior of the FastICA algorithm as a hyperspectral unmixing technique is evaluated using such data. This exploration leads to a number of useful insights such as: (1) the need to retain more dimensions than indicated by eigenvalue analysis to obtain near-optimal results; (2) conditions in which orthogonalization of unmixing vectors is detrimental to the exploitation results; and (3) a means for improving FastICA unmixing results by recognizing and compensating for materials that have been split into multiple abundance maps.

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“…Many techniques have been proposed to estimate the fraction of pure spectra in a given spectrum. The main difficulties reside in constraints on the estimated coefficients in order to represent the physical abundancies, with the sum-to-one and the non-negativity constraints(see [4,5] and references therein).…”

Section: Introductionmentioning

confidence: 99%

A simple scheme for unmixing hyperspectral data based on the geometry of the N-dimensional simplex

Honeiné

Richard²

2010

2010 IEEE International Geoscience and Remote Sensing Symposium

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In this paper, we study the problem of decomposing spectra in hyperspectral data into the sum of pure spectra, or endmembers. We propose to jointly extract the endmembers and estimate the corresponding fractions, or abundances. For this purpose, we show that these abundances can be easily computed using volume of simplices, from the same information used in the classical N-Findr algorithm. This results into a simple scheme for unmixing hyperspectral data, with low computational complexity. Experimental results show the efficiency of the proposed method.

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Algorithm taxonomy for hyperspectral unmixing

Cited by 63 publications

References 32 publications

A comparative analysis of classification methods for hyperspectral images generated with conventional dimension reduction methods

A comparative analysis of classification methods for hyperspectral images generated with conventional dimension reduction methods

Using Physically-Modeled Synthetic Data to Assess Hyperspectral Unmixing Approaches

A simple scheme for unmixing hyperspectral data based on the geometry of the N-dimensional simplex

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