Spatiotemporal image fusion in remote sensing

M. Belgiu (Corresponding Author), A. Stein

Research output: Contribution to journalArticleAcademicpeer-review

7 Downloads (Pure)

Abstract

In this paper, we discuss spatiotemporal data fusion methods in remote sensing. These methods fuse temporally sparse fine-resolution images with temporally dense coarse-resolution images. This review reveals that existing spatiotemporal data fusion methods are mainly dedicated to blending optical images. There is a limited number of studies focusing on fusing microwave data, or on fusing microwave and optical images in order to address the problem of gaps in the optical data caused by the presence of clouds. Therefore, future efforts are required to develop spatiotemporal data fusion methods flexible enough to accomplish different data fusion tasks under different environmental conditions and using different sensors data as input. The review shows that additional investigations are required to account for temporal changes occurring during the observation period when predicting spectral reflectance values at a fine scale in space and time. More sophisticated machine learning methods such as convolutional neural network (CNN) represent a promising solution for spatiotemporal fusion, especially due to their capability to fuse images with different spectral values.

Original languageEnglish
Article number818
Pages (from-to)1-20
Number of pages20
JournalRemote sensing
Volume11
Issue number7
DOIs
Publication statusPublished - 4 Apr 2019

Fingerprint

remote sensing
image resolution
spectral reflectance
environmental conditions
method
sensor
microwave

Keywords

  • Data fusion
  • Time series satellite images
  • ITC-ISI-JOURNAL-ARTICLE
  • ITC-GOLD

Cite this

@article{488b08c806124498a5a25b37cb48887f,
title = "Spatiotemporal image fusion in remote sensing",
abstract = "In this paper, we discuss spatiotemporal data fusion methods in remote sensing. These methods fuse temporally sparse fine-resolution images with temporally dense coarse-resolution images. This review reveals that existing spatiotemporal data fusion methods are mainly dedicated to blending optical images. There is a limited number of studies focusing on fusing microwave data, or on fusing microwave and optical images in order to address the problem of gaps in the optical data caused by the presence of clouds. Therefore, future efforts are required to develop spatiotemporal data fusion methods flexible enough to accomplish different data fusion tasks under different environmental conditions and using different sensors data as input. The review shows that additional investigations are required to account for temporal changes occurring during the observation period when predicting spectral reflectance values at a fine scale in space and time. More sophisticated machine learning methods such as convolutional neural network (CNN) represent a promising solution for spatiotemporal fusion, especially due to their capability to fuse images with different spectral values.",
keywords = "Data fusion, Time series satellite images, ITC-ISI-JOURNAL-ARTICLE, ITC-GOLD",
author = "M. Belgiu and A. Stein",
year = "2019",
month = "4",
day = "4",
doi = "10.3390/rs11070818",
language = "English",
volume = "11",
pages = "1--20",
journal = "Remote sensing",
issn = "2072-4292",
publisher = "Multidisciplinary Digital Publishing Institute",
number = "7",

}

Spatiotemporal image fusion in remote sensing. / Belgiu, M. (Corresponding Author); Stein, A.

In: Remote sensing, Vol. 11, No. 7, 818, 04.04.2019, p. 1-20.

Research output: Contribution to journalArticleAcademicpeer-review

TY - JOUR

T1 - Spatiotemporal image fusion in remote sensing

AU - Belgiu, M.

AU - Stein, A.

PY - 2019/4/4

Y1 - 2019/4/4

N2 - In this paper, we discuss spatiotemporal data fusion methods in remote sensing. These methods fuse temporally sparse fine-resolution images with temporally dense coarse-resolution images. This review reveals that existing spatiotemporal data fusion methods are mainly dedicated to blending optical images. There is a limited number of studies focusing on fusing microwave data, or on fusing microwave and optical images in order to address the problem of gaps in the optical data caused by the presence of clouds. Therefore, future efforts are required to develop spatiotemporal data fusion methods flexible enough to accomplish different data fusion tasks under different environmental conditions and using different sensors data as input. The review shows that additional investigations are required to account for temporal changes occurring during the observation period when predicting spectral reflectance values at a fine scale in space and time. More sophisticated machine learning methods such as convolutional neural network (CNN) represent a promising solution for spatiotemporal fusion, especially due to their capability to fuse images with different spectral values.

AB - In this paper, we discuss spatiotemporal data fusion methods in remote sensing. These methods fuse temporally sparse fine-resolution images with temporally dense coarse-resolution images. This review reveals that existing spatiotemporal data fusion methods are mainly dedicated to blending optical images. There is a limited number of studies focusing on fusing microwave data, or on fusing microwave and optical images in order to address the problem of gaps in the optical data caused by the presence of clouds. Therefore, future efforts are required to develop spatiotemporal data fusion methods flexible enough to accomplish different data fusion tasks under different environmental conditions and using different sensors data as input. The review shows that additional investigations are required to account for temporal changes occurring during the observation period when predicting spectral reflectance values at a fine scale in space and time. More sophisticated machine learning methods such as convolutional neural network (CNN) represent a promising solution for spatiotemporal fusion, especially due to their capability to fuse images with different spectral values.

KW - Data fusion

KW - Time series satellite images

KW - ITC-ISI-JOURNAL-ARTICLE

KW - ITC-GOLD

UR - https://ezproxy2.utwente.nl/login?url=https://doi.org/10.3390/rs11070818

UR - https://ezproxy2.utwente.nl/login?url=https://library.itc.utwente.nl/login/2019/isi/belgiu_spa.pdf

U2 - 10.3390/rs11070818

DO - 10.3390/rs11070818

M3 - Article

VL - 11

SP - 1

EP - 20

JO - Remote sensing

JF - Remote sensing

SN - 2072-4292

IS - 7

M1 - 818

ER -