Abstract

To enhance the efficiency of machine-learning algorithms of optical remote sensing imagery processing, optimization techniques are evolved of the land surface objects pattern recognition. Different methods of supervised classification are considered for these purposes, including the metrical classifier operating with Euclidean distance between any points of the multi-dimensional feature space given by registered spectra, the K-nearest neighbors classifier based on a majority vote for neighboring pixels of the recognized objects, the Bayesian classifier of statistical decision making, the Support Vector Machine classifier dealing with stable solutions of the mini-max optimization problem and their different modifications. We describe the related techniques applied for selected test regions to compare the listed classifiers.

© 2016 Optical Society of America

Full Article  |  PDF Article
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References

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  22. M. Dalponte, L. Bruzzone, and D. Gianelle, “Fusion of hyperspectral and LIDAR remote sensing data for classification of complex forest areas,” IEEE Trans. Geosci. Rem. Sens. 46(5), 1416–1427 (2008).
    [Crossref]

2015 (3)

V. V. Kozoderov, E. V. Dmitriev, and A. A. Sokolov, “Improved technique for retrieval of forest parameters from hyperspectral remote sensing data,” Opt. Express 23(24), A1342–A1353 (2015).
[Crossref] [PubMed]

V. V. Kozoderov, E. V. Dmitriev, and A. A. Sokolov, “Cognitive technologies in optical remote sensing data processing,” Climate Nature 1(2), 5–45 (2015).

V. V. Kozoderov, T. V. Kondranin, E. V. Dmitriev, and V. P. Kamentsev, “Bayesian classifier applications of airborne hyperspectral imagery processing for forested areas,” Adv. Space Res. 55(11), 2657–2667 (2015).
[Crossref]

2014 (3)

V. V. Kozoderov, T. V. Kondranin, E. V. Dmitriev, and A. A. Sokolov, “Retrieval of forest stand attributes using optical airborne remote sensing data,” Opt. Express 22(13), 15410–15423 (2014).
[Crossref] [PubMed]

M. Dalponte, L. T. Ene, H. O. Orka, T. Gobakken, and E. Naesset, “Unsupervised selection of training samples for tree species classification using hyperspectral data,” IEEE J. Sel. Top. Appl. Earth Observations Remote Sens. 7(8), 3560–3569 (2014).
[Crossref]

V. V. Kozoderov, T. V. Kondranin, E. V. Dmitriev, and V. P. Kamentsev, “A system for processing hyperspectral imagery: application to detecting forest species,” Int. J. Remote Sens. 35(15), 5926–5945 (2014).

2013 (1)

P. Pant, V. Heikkinen, A. Hovi, I. Korpela, M. Hauta-Kasari, and T. Tokola, “Evaluation of simulated bands in airborne optical sensors for tree species identification,” Remote Sens. Environ. 138, 27–37 (2013).
[Crossref]

2012 (1)

G.-X. Yuan, C.-H. Ho, and C.-J. Lin, “Recent advances of large-scale linear classification,” Proc. IEEE 100(9), 2584–2603 (2012).
[Crossref]

2009 (1)

A. Plaza, J. A. Benediktsson, J. W. Boardman, J. Brazile, L. Bruzzone, G. Camps-Valls, J. Chanussot, M. Fauvel, P. Gamba, A. Gualtieri, M. Marconcini, J. C. Tilton, and G. Trianni, “Recent advances in techniques for hyperspectral image processing,” Remote Sens. Environ. 113, S110–S122 (2009).
[Crossref]

2008 (1)

M. Dalponte, L. Bruzzone, and D. Gianelle, “Fusion of hyperspectral and LIDAR remote sensing data for classification of complex forest areas,” IEEE Trans. Geosci. Rem. Sens. 46(5), 1416–1427 (2008).
[Crossref]

2006 (1)

G. Camps-Valls, L. Gomez-Chova, J. Muñoz-Mari, J. Vila-Frances, and J. Calpe-Maravilla, “Composite kernels for hyperspectral image classification,” IEEE Geosci. Remote Sens. Lett. 3(1), 93–97 (2006).
[Crossref]

2004 (1)

R. Haapanen, A. R. Ek, M. E. Bauer, and A. O. Finley, “Delineation of forest/nonforest land use classes using nearest neighbor methods,” Remote Sens. Environ. 89(3), 265–271 (2004).
[Crossref]

2000 (2)

G. F. Golub and H. A. van der Vorst, “Eigenvalue computation in the 20th century,” J. Comput. Appl. Math. 123(1-2), 35–65 (2000).
[Crossref]

V. Vapnik and O. Chapelle, “Bounds on error expectation for support vector machines,” Neural Comput. 12(9), 2013–2036 (2000).
[Crossref] [PubMed]

1993 (1)

S. Cost and S. Salzberg, “A weighted nearest neighbor algorithm for learning with symbolic features,” Mach. Learn. 10(1), 57–78 (1993).
[Crossref]

1989 (1)

J. Besag, “Towards Bayesian image analysis,” J. Appl. Stat. 16(3), 395–406 (1989).
[Crossref]

1962 (1)

E. Parzen, “On the estimation of a probability density function and the mode,” Ann. Math. Stat. 33(3), 1065–1076 (1962).
[Crossref]

Bauer, M. E.

R. Haapanen, A. R. Ek, M. E. Bauer, and A. O. Finley, “Delineation of forest/nonforest land use classes using nearest neighbor methods,” Remote Sens. Environ. 89(3), 265–271 (2004).
[Crossref]

Benediktsson, J. A.

A. Plaza, J. A. Benediktsson, J. W. Boardman, J. Brazile, L. Bruzzone, G. Camps-Valls, J. Chanussot, M. Fauvel, P. Gamba, A. Gualtieri, M. Marconcini, J. C. Tilton, and G. Trianni, “Recent advances in techniques for hyperspectral image processing,” Remote Sens. Environ. 113, S110–S122 (2009).
[Crossref]

Besag, J.

J. Besag, “Towards Bayesian image analysis,” J. Appl. Stat. 16(3), 395–406 (1989).
[Crossref]

Boardman, J. W.

A. Plaza, J. A. Benediktsson, J. W. Boardman, J. Brazile, L. Bruzzone, G. Camps-Valls, J. Chanussot, M. Fauvel, P. Gamba, A. Gualtieri, M. Marconcini, J. C. Tilton, and G. Trianni, “Recent advances in techniques for hyperspectral image processing,” Remote Sens. Environ. 113, S110–S122 (2009).
[Crossref]

Brazile, J.

A. Plaza, J. A. Benediktsson, J. W. Boardman, J. Brazile, L. Bruzzone, G. Camps-Valls, J. Chanussot, M. Fauvel, P. Gamba, A. Gualtieri, M. Marconcini, J. C. Tilton, and G. Trianni, “Recent advances in techniques for hyperspectral image processing,” Remote Sens. Environ. 113, S110–S122 (2009).
[Crossref]

Bruzzone, L.

A. Plaza, J. A. Benediktsson, J. W. Boardman, J. Brazile, L. Bruzzone, G. Camps-Valls, J. Chanussot, M. Fauvel, P. Gamba, A. Gualtieri, M. Marconcini, J. C. Tilton, and G. Trianni, “Recent advances in techniques for hyperspectral image processing,” Remote Sens. Environ. 113, S110–S122 (2009).
[Crossref]

M. Dalponte, L. Bruzzone, and D. Gianelle, “Fusion of hyperspectral and LIDAR remote sensing data for classification of complex forest areas,” IEEE Trans. Geosci. Rem. Sens. 46(5), 1416–1427 (2008).
[Crossref]

Calpe-Maravilla, J.

G. Camps-Valls, L. Gomez-Chova, J. Muñoz-Mari, J. Vila-Frances, and J. Calpe-Maravilla, “Composite kernels for hyperspectral image classification,” IEEE Geosci. Remote Sens. Lett. 3(1), 93–97 (2006).
[Crossref]

Camps-Valls, G.

A. Plaza, J. A. Benediktsson, J. W. Boardman, J. Brazile, L. Bruzzone, G. Camps-Valls, J. Chanussot, M. Fauvel, P. Gamba, A. Gualtieri, M. Marconcini, J. C. Tilton, and G. Trianni, “Recent advances in techniques for hyperspectral image processing,” Remote Sens. Environ. 113, S110–S122 (2009).
[Crossref]

G. Camps-Valls, L. Gomez-Chova, J. Muñoz-Mari, J. Vila-Frances, and J. Calpe-Maravilla, “Composite kernels for hyperspectral image classification,” IEEE Geosci. Remote Sens. Lett. 3(1), 93–97 (2006).
[Crossref]

Chanussot, J.

A. Plaza, J. A. Benediktsson, J. W. Boardman, J. Brazile, L. Bruzzone, G. Camps-Valls, J. Chanussot, M. Fauvel, P. Gamba, A. Gualtieri, M. Marconcini, J. C. Tilton, and G. Trianni, “Recent advances in techniques for hyperspectral image processing,” Remote Sens. Environ. 113, S110–S122 (2009).
[Crossref]

Chapelle, O.

V. Vapnik and O. Chapelle, “Bounds on error expectation for support vector machines,” Neural Comput. 12(9), 2013–2036 (2000).
[Crossref] [PubMed]

Cost, S.

S. Cost and S. Salzberg, “A weighted nearest neighbor algorithm for learning with symbolic features,” Mach. Learn. 10(1), 57–78 (1993).
[Crossref]

Dalponte, M.

M. Dalponte, L. T. Ene, H. O. Orka, T. Gobakken, and E. Naesset, “Unsupervised selection of training samples for tree species classification using hyperspectral data,” IEEE J. Sel. Top. Appl. Earth Observations Remote Sens. 7(8), 3560–3569 (2014).
[Crossref]

M. Dalponte, L. Bruzzone, and D. Gianelle, “Fusion of hyperspectral and LIDAR remote sensing data for classification of complex forest areas,” IEEE Trans. Geosci. Rem. Sens. 46(5), 1416–1427 (2008).
[Crossref]

Dmitriev, E. V.

V. V. Kozoderov, E. V. Dmitriev, and A. A. Sokolov, “Improved technique for retrieval of forest parameters from hyperspectral remote sensing data,” Opt. Express 23(24), A1342–A1353 (2015).
[Crossref] [PubMed]

V. V. Kozoderov, E. V. Dmitriev, and A. A. Sokolov, “Cognitive technologies in optical remote sensing data processing,” Climate Nature 1(2), 5–45 (2015).

V. V. Kozoderov, T. V. Kondranin, E. V. Dmitriev, and V. P. Kamentsev, “Bayesian classifier applications of airborne hyperspectral imagery processing for forested areas,” Adv. Space Res. 55(11), 2657–2667 (2015).
[Crossref]

V. V. Kozoderov, T. V. Kondranin, E. V. Dmitriev, and A. A. Sokolov, “Retrieval of forest stand attributes using optical airborne remote sensing data,” Opt. Express 22(13), 15410–15423 (2014).
[Crossref] [PubMed]

V. V. Kozoderov, T. V. Kondranin, E. V. Dmitriev, and V. P. Kamentsev, “A system for processing hyperspectral imagery: application to detecting forest species,” Int. J. Remote Sens. 35(15), 5926–5945 (2014).

Ek, A. R.

R. Haapanen, A. R. Ek, M. E. Bauer, and A. O. Finley, “Delineation of forest/nonforest land use classes using nearest neighbor methods,” Remote Sens. Environ. 89(3), 265–271 (2004).
[Crossref]

Ene, L. T.

M. Dalponte, L. T. Ene, H. O. Orka, T. Gobakken, and E. Naesset, “Unsupervised selection of training samples for tree species classification using hyperspectral data,” IEEE J. Sel. Top. Appl. Earth Observations Remote Sens. 7(8), 3560–3569 (2014).
[Crossref]

Fauvel, M.

A. Plaza, J. A. Benediktsson, J. W. Boardman, J. Brazile, L. Bruzzone, G. Camps-Valls, J. Chanussot, M. Fauvel, P. Gamba, A. Gualtieri, M. Marconcini, J. C. Tilton, and G. Trianni, “Recent advances in techniques for hyperspectral image processing,” Remote Sens. Environ. 113, S110–S122 (2009).
[Crossref]

Finley, A. O.

R. Haapanen, A. R. Ek, M. E. Bauer, and A. O. Finley, “Delineation of forest/nonforest land use classes using nearest neighbor methods,” Remote Sens. Environ. 89(3), 265–271 (2004).
[Crossref]

Gamba, P.

A. Plaza, J. A. Benediktsson, J. W. Boardman, J. Brazile, L. Bruzzone, G. Camps-Valls, J. Chanussot, M. Fauvel, P. Gamba, A. Gualtieri, M. Marconcini, J. C. Tilton, and G. Trianni, “Recent advances in techniques for hyperspectral image processing,” Remote Sens. Environ. 113, S110–S122 (2009).
[Crossref]

Gianelle, D.

M. Dalponte, L. Bruzzone, and D. Gianelle, “Fusion of hyperspectral and LIDAR remote sensing data for classification of complex forest areas,” IEEE Trans. Geosci. Rem. Sens. 46(5), 1416–1427 (2008).
[Crossref]

Gobakken, T.

M. Dalponte, L. T. Ene, H. O. Orka, T. Gobakken, and E. Naesset, “Unsupervised selection of training samples for tree species classification using hyperspectral data,” IEEE J. Sel. Top. Appl. Earth Observations Remote Sens. 7(8), 3560–3569 (2014).
[Crossref]

Golub, G. F.

G. F. Golub and H. A. van der Vorst, “Eigenvalue computation in the 20th century,” J. Comput. Appl. Math. 123(1-2), 35–65 (2000).
[Crossref]

Gomez-Chova, L.

G. Camps-Valls, L. Gomez-Chova, J. Muñoz-Mari, J. Vila-Frances, and J. Calpe-Maravilla, “Composite kernels for hyperspectral image classification,” IEEE Geosci. Remote Sens. Lett. 3(1), 93–97 (2006).
[Crossref]

Gualtieri, A.

A. Plaza, J. A. Benediktsson, J. W. Boardman, J. Brazile, L. Bruzzone, G. Camps-Valls, J. Chanussot, M. Fauvel, P. Gamba, A. Gualtieri, M. Marconcini, J. C. Tilton, and G. Trianni, “Recent advances in techniques for hyperspectral image processing,” Remote Sens. Environ. 113, S110–S122 (2009).
[Crossref]

Haapanen, R.

R. Haapanen, A. R. Ek, M. E. Bauer, and A. O. Finley, “Delineation of forest/nonforest land use classes using nearest neighbor methods,” Remote Sens. Environ. 89(3), 265–271 (2004).
[Crossref]

Hauta-Kasari, M.

P. Pant, V. Heikkinen, A. Hovi, I. Korpela, M. Hauta-Kasari, and T. Tokola, “Evaluation of simulated bands in airborne optical sensors for tree species identification,” Remote Sens. Environ. 138, 27–37 (2013).
[Crossref]

Heikkinen, V.

P. Pant, V. Heikkinen, A. Hovi, I. Korpela, M. Hauta-Kasari, and T. Tokola, “Evaluation of simulated bands in airborne optical sensors for tree species identification,” Remote Sens. Environ. 138, 27–37 (2013).
[Crossref]

Ho, C.-H.

G.-X. Yuan, C.-H. Ho, and C.-J. Lin, “Recent advances of large-scale linear classification,” Proc. IEEE 100(9), 2584–2603 (2012).
[Crossref]

Hovi, A.

P. Pant, V. Heikkinen, A. Hovi, I. Korpela, M. Hauta-Kasari, and T. Tokola, “Evaluation of simulated bands in airborne optical sensors for tree species identification,” Remote Sens. Environ. 138, 27–37 (2013).
[Crossref]

Kamentsev, V. P.

V. V. Kozoderov, T. V. Kondranin, E. V. Dmitriev, and V. P. Kamentsev, “Bayesian classifier applications of airborne hyperspectral imagery processing for forested areas,” Adv. Space Res. 55(11), 2657–2667 (2015).
[Crossref]

V. V. Kozoderov, T. V. Kondranin, E. V. Dmitriev, and V. P. Kamentsev, “A system for processing hyperspectral imagery: application to detecting forest species,” Int. J. Remote Sens. 35(15), 5926–5945 (2014).

Kondranin, T. V.

V. V. Kozoderov, T. V. Kondranin, E. V. Dmitriev, and V. P. Kamentsev, “Bayesian classifier applications of airborne hyperspectral imagery processing for forested areas,” Adv. Space Res. 55(11), 2657–2667 (2015).
[Crossref]

V. V. Kozoderov, T. V. Kondranin, E. V. Dmitriev, and V. P. Kamentsev, “A system for processing hyperspectral imagery: application to detecting forest species,” Int. J. Remote Sens. 35(15), 5926–5945 (2014).

V. V. Kozoderov, T. V. Kondranin, E. V. Dmitriev, and A. A. Sokolov, “Retrieval of forest stand attributes using optical airborne remote sensing data,” Opt. Express 22(13), 15410–15423 (2014).
[Crossref] [PubMed]

Korpela, I.

P. Pant, V. Heikkinen, A. Hovi, I. Korpela, M. Hauta-Kasari, and T. Tokola, “Evaluation of simulated bands in airborne optical sensors for tree species identification,” Remote Sens. Environ. 138, 27–37 (2013).
[Crossref]

Kozoderov, V. V.

V. V. Kozoderov, E. V. Dmitriev, and A. A. Sokolov, “Improved technique for retrieval of forest parameters from hyperspectral remote sensing data,” Opt. Express 23(24), A1342–A1353 (2015).
[Crossref] [PubMed]

V. V. Kozoderov, E. V. Dmitriev, and A. A. Sokolov, “Cognitive technologies in optical remote sensing data processing,” Climate Nature 1(2), 5–45 (2015).

V. V. Kozoderov, T. V. Kondranin, E. V. Dmitriev, and V. P. Kamentsev, “Bayesian classifier applications of airborne hyperspectral imagery processing for forested areas,” Adv. Space Res. 55(11), 2657–2667 (2015).
[Crossref]

V. V. Kozoderov, T. V. Kondranin, E. V. Dmitriev, and A. A. Sokolov, “Retrieval of forest stand attributes using optical airborne remote sensing data,” Opt. Express 22(13), 15410–15423 (2014).
[Crossref] [PubMed]

V. V. Kozoderov, T. V. Kondranin, E. V. Dmitriev, and V. P. Kamentsev, “A system for processing hyperspectral imagery: application to detecting forest species,” Int. J. Remote Sens. 35(15), 5926–5945 (2014).

Lin, C.-J.

G.-X. Yuan, C.-H. Ho, and C.-J. Lin, “Recent advances of large-scale linear classification,” Proc. IEEE 100(9), 2584–2603 (2012).
[Crossref]

Marconcini, M.

A. Plaza, J. A. Benediktsson, J. W. Boardman, J. Brazile, L. Bruzzone, G. Camps-Valls, J. Chanussot, M. Fauvel, P. Gamba, A. Gualtieri, M. Marconcini, J. C. Tilton, and G. Trianni, “Recent advances in techniques for hyperspectral image processing,” Remote Sens. Environ. 113, S110–S122 (2009).
[Crossref]

Muñoz-Mari, J.

G. Camps-Valls, L. Gomez-Chova, J. Muñoz-Mari, J. Vila-Frances, and J. Calpe-Maravilla, “Composite kernels for hyperspectral image classification,” IEEE Geosci. Remote Sens. Lett. 3(1), 93–97 (2006).
[Crossref]

Naesset, E.

M. Dalponte, L. T. Ene, H. O. Orka, T. Gobakken, and E. Naesset, “Unsupervised selection of training samples for tree species classification using hyperspectral data,” IEEE J. Sel. Top. Appl. Earth Observations Remote Sens. 7(8), 3560–3569 (2014).
[Crossref]

Orka, H. O.

M. Dalponte, L. T. Ene, H. O. Orka, T. Gobakken, and E. Naesset, “Unsupervised selection of training samples for tree species classification using hyperspectral data,” IEEE J. Sel. Top. Appl. Earth Observations Remote Sens. 7(8), 3560–3569 (2014).
[Crossref]

Pant, P.

P. Pant, V. Heikkinen, A. Hovi, I. Korpela, M. Hauta-Kasari, and T. Tokola, “Evaluation of simulated bands in airborne optical sensors for tree species identification,” Remote Sens. Environ. 138, 27–37 (2013).
[Crossref]

Parzen, E.

E. Parzen, “On the estimation of a probability density function and the mode,” Ann. Math. Stat. 33(3), 1065–1076 (1962).
[Crossref]

Plaza, A.

A. Plaza, J. A. Benediktsson, J. W. Boardman, J. Brazile, L. Bruzzone, G. Camps-Valls, J. Chanussot, M. Fauvel, P. Gamba, A. Gualtieri, M. Marconcini, J. C. Tilton, and G. Trianni, “Recent advances in techniques for hyperspectral image processing,” Remote Sens. Environ. 113, S110–S122 (2009).
[Crossref]

Salzberg, S.

S. Cost and S. Salzberg, “A weighted nearest neighbor algorithm for learning with symbolic features,” Mach. Learn. 10(1), 57–78 (1993).
[Crossref]

Sokolov, A. A.

Tilton, J. C.

A. Plaza, J. A. Benediktsson, J. W. Boardman, J. Brazile, L. Bruzzone, G. Camps-Valls, J. Chanussot, M. Fauvel, P. Gamba, A. Gualtieri, M. Marconcini, J. C. Tilton, and G. Trianni, “Recent advances in techniques for hyperspectral image processing,” Remote Sens. Environ. 113, S110–S122 (2009).
[Crossref]

Tokola, T.

P. Pant, V. Heikkinen, A. Hovi, I. Korpela, M. Hauta-Kasari, and T. Tokola, “Evaluation of simulated bands in airborne optical sensors for tree species identification,” Remote Sens. Environ. 138, 27–37 (2013).
[Crossref]

Trianni, G.

A. Plaza, J. A. Benediktsson, J. W. Boardman, J. Brazile, L. Bruzzone, G. Camps-Valls, J. Chanussot, M. Fauvel, P. Gamba, A. Gualtieri, M. Marconcini, J. C. Tilton, and G. Trianni, “Recent advances in techniques for hyperspectral image processing,” Remote Sens. Environ. 113, S110–S122 (2009).
[Crossref]

van der Vorst, H. A.

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Figures (6)

Fig. 1
Fig. 1

RGB image of the test area (a) and its representation in the near-infrared spectral channel with the central wavelength 827 nm (b).

Fig. 2
Fig. 2

The eigenvalues of the image radiometrically corrected for all spectral channels. Outlined is the noise level of the imaging spectrometer. Shown by arrow is the 5-th principal component that is behind the noise level.

Fig. 3
Fig. 3

Informative (PC 1,2,4) and noise (PC 5) principal components of the corrected image decomposition based on the empirical orthogonal basis of functions.

Fig. 4
Fig. 4

RGB-synthesized image (a) and the results of its classification by the metric classifier (b) and the K weighted neighborhood classifier (c). Colors on b and c pictures: blue – water, yellow – sand, black – asphalt, dark green – pine forests, light green – birch forests, orange – aspen forests, red – grasses, violet – other objects.

Fig. 5
Fig. 5

Classification results of a hyperspectral image by different approaches: a – RGB-synthesized image, b – the metrical classifier (dealing with Euclidian distance), c – the KNN classifier, d – the SVM classifier with Gaussian kernel, e – the SVM classifier with polynomial kernel, f – the SVM classifier with linear kernel, g – the linear Bayesian classifier, h – the normal Bayesian classifier, i – the Bayesian classifier operating with Gaussian mixture model.

Fig. 6
Fig. 6

The recognition results of species composition obtained by different classifiers: the SVM with Gaussian kernel (a), the metrical classifier (b), the Bayesian classifier with Gaussian mixture model (c), the KNN classifier (d). Numbers near to the classifier notations show the total amount of pixels with erroneous classification. The white lines on each scene delineate the ground-based 12 forest inventory plots while the next figures in white gives information about the percentage cover of the related species within each plot. Numbers on the color scale denote gradations of the solar illumination from completely shaded pixels (1) to the sunlit tree’s tops (3) with intermediate illumination condition (2). Pixels belonging to other and unrecognized objects are selected by different colors.

Tables (1)

Tables Icon

Table 1 Similarity of the classification results by different methods: MC – the metrical classifier with Euclidian distance; BCG – the Bayesian classifier with Gaussian mixture of spectral radiances; BCL – the linear Bayesian classifier; BCN – the normal Bayesian classifier; SVML – the SVM classifier with linear kernel; SVMS – with square kernel; SVMG – with Gaussian kernel.

Metrics