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Available under license: CC BY-NC: Creative Commons Attribution-NonCommercial 4.0 International License
Final published version
Research output: Contribution to Journal/Magazine › Journal article › peer-review
Multisource and multitemporal data fusion in remote sensing : A comprehensive review of the state of the art. / Ghamisi, P.; Rasti, B.; Yokoya, N. et al.
In: IEEE Geoscience and Remote Sensing Magazine, Vol. 7, No. 1, 01.03.2019, p. 6-39.Research output: Contribution to Journal/Magazine › Journal article › peer-review
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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 -