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Multisource and multitemporal data fusion in remote sensing: A comprehensive review of the state of the art

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Multisource and multitemporal data fusion in remote sensing : A comprehensive review of the state of the art. / Ghamisi, P.; Rasti, B.; Yokoya, N.; Wang, Q.; Hofle, B.; Bruzzone, L.; Bovolo, F.; Chi, M.; Anders, K.; Gloaguen, R.; Atkinson, P.M.; Benediktsson, J.A.

In: IEEE Geoscience and Remote Sensing Magazine, Vol. 7, No. 1, 01.03.2019, p. 6-39.

Research output: Contribution to journalJournal articlepeer-review

Harvard

Ghamisi, P, Rasti, B, Yokoya, N, Wang, Q, Hofle, B, Bruzzone, L, Bovolo, F, Chi, M, Anders, K, Gloaguen, R, Atkinson, PM & Benediktsson, JA 2019, 'Multisource and multitemporal data fusion in remote sensing: A comprehensive review of the state of the art', IEEE Geoscience and Remote Sensing Magazine, vol. 7, no. 1, pp. 6-39. https://doi.org/10.1109/MGRS.2018.2890023

APA

Ghamisi, P., Rasti, B., Yokoya, N., Wang, Q., Hofle, B., Bruzzone, L., Bovolo, F., Chi, M., Anders, K., Gloaguen, R., Atkinson, P. M., & Benediktsson, J. A. (2019). Multisource and multitemporal data fusion in remote sensing: A comprehensive review of the state of the art. IEEE Geoscience and Remote Sensing Magazine, 7(1), 6-39. https://doi.org/10.1109/MGRS.2018.2890023

Vancouver

Ghamisi P, Rasti B, Yokoya N, Wang Q, Hofle B, Bruzzone L et al. Multisource and multitemporal data fusion in remote sensing: A comprehensive review of the state of the art. IEEE Geoscience and Remote Sensing Magazine. 2019 Mar 1;7(1):6-39. https://doi.org/10.1109/MGRS.2018.2890023

Author

Ghamisi, P. ; Rasti, B. ; Yokoya, N. ; Wang, Q. ; Hofle, B. ; Bruzzone, L. ; Bovolo, F. ; Chi, M. ; Anders, K. ; Gloaguen, R. ; Atkinson, P.M. ; Benediktsson, J.A. / Multisource and multitemporal data fusion in remote sensing : A comprehensive review of the state of the art. In: IEEE Geoscience and Remote Sensing Magazine. 2019 ; Vol. 7, No. 1. pp. 6-39.

Bibtex

@article{30b77810e688478597ef96ff7bd3a4cb,
title = "Multisource and multitemporal data fusion in remote sensing: A comprehensive review of the state of the art",
abstract = "The recent, sharp increase in the availability of data captured by different sensors, combined with their considerable heterogeneity, poses a serious challenge for the effective and efficient processing of remotely sensed data. Such an increase in remote sensing and ancillary data sets, however, opens up the possibility of utilizing multimodal data sets in a joint manner to further improve the performance of the processing approaches with respect to applications at hand. Multisource data fusion has, therefore, received enormous attention from researchers worldwide for a wide variety of applications. Moreover, thanks to the revisit capability of several.",
keywords = "Data fusion, Ancillary data, Multi-modal data, Multi-temporal data, Multisource data, Processing approach, Remotely sensed data, Sharp increase, State of the art, Remote sensing",
author = "P. Ghamisi and B. Rasti and N. Yokoya and Q. Wang and B. Hofle and L. Bruzzone and F. Bovolo and M. Chi and K. Anders and R. Gloaguen and P.M. Atkinson and J.A. Benediktsson",
note = "{\textcopyright}2019 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. ",
year = "2019",
month = mar,
day = "1",
doi = "10.1109/MGRS.2018.2890023",
language = "English",
volume = "7",
pages = "6--39",
journal = "IEEE Geoscience and Remote Sensing Magazine",
issn = "2473-2397",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
number = "1",

}

RIS

TY - JOUR

T1 - Multisource and multitemporal data fusion in remote sensing

T2 - A comprehensive review of the state of the art

AU - Ghamisi, P.

AU - Rasti, B.

AU - Yokoya, N.

AU - Wang, Q.

AU - Hofle, B.

AU - Bruzzone, L.

AU - Bovolo, F.

AU - Chi, M.

AU - Anders, K.

AU - Gloaguen, R.

AU - Atkinson, P.M.

AU - Benediktsson, J.A.

N1 - ©2019 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

PY - 2019/3/1

Y1 - 2019/3/1

N2 - The recent, sharp increase in the availability of data captured by different sensors, combined with their considerable heterogeneity, poses a serious challenge for the effective and efficient processing of remotely sensed data. Such an increase in remote sensing and ancillary data sets, however, opens up the possibility of utilizing multimodal data sets in a joint manner to further improve the performance of the processing approaches with respect to applications at hand. Multisource data fusion has, therefore, received enormous attention from researchers worldwide for a wide variety of applications. Moreover, thanks to the revisit capability of several.

AB - The recent, sharp increase in the availability of data captured by different sensors, combined with their considerable heterogeneity, poses a serious challenge for the effective and efficient processing of remotely sensed data. Such an increase in remote sensing and ancillary data sets, however, opens up the possibility of utilizing multimodal data sets in a joint manner to further improve the performance of the processing approaches with respect to applications at hand. Multisource data fusion has, therefore, received enormous attention from researchers worldwide for a wide variety of applications. Moreover, thanks to the revisit capability of several.

KW - Data fusion

KW - Ancillary data

KW - Multi-modal data

KW - Multi-temporal data

KW - Multisource data

KW - Processing approach

KW - Remotely sensed data

KW - Sharp increase

KW - State of the art

KW - Remote sensing

U2 - 10.1109/MGRS.2018.2890023

DO - 10.1109/MGRS.2018.2890023

M3 - Journal article

VL - 7

SP - 6

EP - 39

JO - IEEE Geoscience and Remote Sensing Magazine

JF - IEEE Geoscience and Remote Sensing Magazine

SN - 2473-2397

IS - 1

ER -