New Frontiers in Spectral-Spatial Hyperspectral Image Classification: The Latest Advances Based on Mathematical Morphology, Markov Random Fields, Segmentation, Sparse Representation, and Deep Learning

Pedram Ghamisi; Emmanuel Maggiori; Shutao Li; Roberto Souza; Yuliya Tarabalka; Gabriele Moser; Andrea De Giorgi; Leyuan Fang; Yushi Chen; Mingmin Chi; Sebastiano B. Serpico; Jon Atli Benediktsson

doi:10.1109/MGRS.2018.2854840

New Frontiers in Spectral-Spatial Hyperspectral Image Classification: The Latest Advances Based on Mathematical Morphology, Markov Random Fields, Segmentation, Sparse Representation, and Deep Learning

Pedram Ghamisi
, Emmanuel Maggiori
, Shutao Li
, Roberto Souza
, Yuliya Tarabalka
, Gabriele Moser
, Andrea De Giorgi
, Leyuan Fang
, Yushi Chen
, Mingmin Chi
, Sebastiano B. Serpico
, Jon Atli Benediktsson

Helmholtz-Zentrum Dresden-Rossendorf
Inria Sophia Antipolis
Hunan University
Seaman Family MR Centre
University of Genoa
School of Electronics and Information Engineering, Harbin Institute of Technology
Fudan University
University of Iceland

Research output: Contribution to specialist publication › Article

325 Scopus citations

Abstract

In recent years, airborne and spaceborne hyperspectral imaging systems have advanced in terms of spectral and spatial resolution, which makes the data sets they produce a valuable source for land cover classification. The availability of hyperspectral data with fine spatial resolution has revolutionized hyperspectral image (HSI) classification techniques by taking advantage of both spectral and spatial information in a single classification framework.

Original language	English
Pages	10-43
Number of pages	34
Volume	6
No	3
Specialist publication	IEEE Geoscience and Remote Sensing Magazine
DOIs	https://doi.org/10.1109/MGRS.2018.2854840
State	Published - Sep 2018
Externally published	Yes

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10.1109/MGRS.2018.2854840

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Ghamisi, P., Maggiori, E., Li, S., Souza, R., Tarabalka, Y., Moser, G., De Giorgi, A., Fang, L., Chen, Y., Chi, M., Serpico, S. B., & Benediktsson, J. A. (2018). New Frontiers in Spectral-Spatial Hyperspectral Image Classification: The Latest Advances Based on Mathematical Morphology, Markov Random Fields, Segmentation, Sparse Representation, and Deep Learning. IEEE Geoscience and Remote Sensing Magazine, 6(3), 10-43. https://doi.org/10.1109/MGRS.2018.2854840

@misc{e078fee5478448ab9ed99ccecc505955,

title = "New Frontiers in Spectral-Spatial Hyperspectral Image Classification: The Latest Advances Based on Mathematical Morphology, Markov Random Fields, Segmentation, Sparse Representation, and Deep Learning",

abstract = "In recent years, airborne and spaceborne hyperspectral imaging systems have advanced in terms of spectral and spatial resolution, which makes the data sets they produce a valuable source for land cover classification. The availability of hyperspectral data with fine spatial resolution has revolutionized hyperspectral image (HSI) classification techniques by taking advantage of both spectral and spatial information in a single classification framework.",

author = "Pedram Ghamisi and Emmanuel Maggiori and Shutao Li and Roberto Souza and Yuliya Tarabalka and Gabriele Moser and \{De Giorgi\}, Andrea and Leyuan Fang and Yushi Chen and Mingmin Chi and Serpico, \{Sebastiano B.\} and Benediktsson, \{Jon Atli\}",

note = "Publisher Copyright: {\textcopyright} 2013 IEEE.",

year = "2018",

month = sep,

doi = "10.1109/MGRS.2018.2854840",

language = "英语",

volume = "6",

pages = "10--43",

journal = "IEEE Geoscience and Remote Sensing Magazine",

issn = "2473-2397",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

}

Ghamisi, P, Maggiori, E, Li, S, Souza, R, Tarabalka, Y, Moser, G, De Giorgi, A, Fang, L, Chen, Y, Chi, M, Serpico, SB & Benediktsson, JA 2018, 'New Frontiers in Spectral-Spatial Hyperspectral Image Classification: The Latest Advances Based on Mathematical Morphology, Markov Random Fields, Segmentation, Sparse Representation, and Deep Learning' IEEE Geoscience and Remote Sensing Magazine, vol. 6, no. 3, pp. 10-43. https://doi.org/10.1109/MGRS.2018.2854840

New Frontiers in Spectral-Spatial Hyperspectral Image Classification: The Latest Advances Based on Mathematical Morphology, Markov Random Fields, Segmentation, Sparse Representation, and Deep Learning. / Ghamisi, Pedram; Maggiori, Emmanuel; Li, Shutao et al.
In: IEEE Geoscience and Remote Sensing Magazine, Vol. 6, No. 3, 09.2018, p. 10-43.

Research output: Contribution to specialist publication › Article

TY - GEN

T1 - New Frontiers in Spectral-Spatial Hyperspectral Image Classification

T2 - The Latest Advances Based on Mathematical Morphology, Markov Random Fields, Segmentation, Sparse Representation, and Deep Learning

AU - Ghamisi, Pedram

AU - Maggiori, Emmanuel

AU - Li, Shutao

AU - Souza, Roberto

AU - Tarabalka, Yuliya

AU - Moser, Gabriele

AU - De Giorgi, Andrea

AU - Fang, Leyuan

AU - Chen, Yushi

AU - Chi, Mingmin

AU - Serpico, Sebastiano B.

AU - Benediktsson, Jon Atli

PY - 2018/9

Y1 - 2018/9

N2 - In recent years, airborne and spaceborne hyperspectral imaging systems have advanced in terms of spectral and spatial resolution, which makes the data sets they produce a valuable source for land cover classification. The availability of hyperspectral data with fine spatial resolution has revolutionized hyperspectral image (HSI) classification techniques by taking advantage of both spectral and spatial information in a single classification framework.

AB - In recent years, airborne and spaceborne hyperspectral imaging systems have advanced in terms of spectral and spatial resolution, which makes the data sets they produce a valuable source for land cover classification. The availability of hyperspectral data with fine spatial resolution has revolutionized hyperspectral image (HSI) classification techniques by taking advantage of both spectral and spatial information in a single classification framework.

UR - https://www.scopus.com/pages/publications/85054521559

U2 - 10.1109/MGRS.2018.2854840

DO - 10.1109/MGRS.2018.2854840

M3 - 文章

AN - SCOPUS:85054521559

SN - 2473-2397

VL - 6

SP - 10

EP - 43

JO - IEEE Geoscience and Remote Sensing Magazine

JF - IEEE Geoscience and Remote Sensing Magazine

ER -

New Frontiers in Spectral-Spatial Hyperspectral Image Classification: The Latest Advances Based on Mathematical Morphology, Markov Random Fields, Segmentation, Sparse Representation, and Deep Learning

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