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Geographically Weighted Spatial Unmixing for Spatiotemporal Fusion

Research output: Contribution to Journal/MagazineJournal articlepeer-review

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Geographically Weighted Spatial Unmixing for Spatiotemporal Fusion. / Peng, K.; Wang, Q.; Tang, Y. et al.
In: IEEE Transactions on Geoscience and Remote Sensing, Vol. 60, 5404217, 31.01.2022.

Research output: Contribution to Journal/MagazineJournal articlepeer-review

Harvard

Peng, K, Wang, Q, Tang, Y, Tong, X & Atkinson, PM 2022, 'Geographically Weighted Spatial Unmixing for Spatiotemporal Fusion', IEEE Transactions on Geoscience and Remote Sensing, vol. 60, 5404217. https://doi.org/10.1109/TGRS.2021.3115136

APA

Peng, K., Wang, Q., Tang, Y., Tong, X., & Atkinson, P. M. (2022). Geographically Weighted Spatial Unmixing for Spatiotemporal Fusion. IEEE Transactions on Geoscience and Remote Sensing, 60, Article 5404217. https://doi.org/10.1109/TGRS.2021.3115136

Vancouver

Peng K, Wang Q, Tang Y, Tong X, Atkinson PM. Geographically Weighted Spatial Unmixing for Spatiotemporal Fusion. IEEE Transactions on Geoscience and Remote Sensing. 2022 Jan 31;60:5404217. Epub 2021 Oct 6. doi: 10.1109/TGRS.2021.3115136

Author

Peng, K. ; Wang, Q. ; Tang, Y. et al. / Geographically Weighted Spatial Unmixing for Spatiotemporal Fusion. In: IEEE Transactions on Geoscience and Remote Sensing. 2022 ; Vol. 60.

Bibtex

@article{35f778d32fbc4b5398926d059e2a8caa,
title = "Geographically Weighted Spatial Unmixing for Spatiotemporal Fusion",
abstract = "Spatiotemporal fusion is a technique applied to create images with both fine spatial and temporal resolutions by blending images with different spatial and temporal resolutions. Spatial unmixing (SU) is a widely used approach for spatiotemporal fusion, which requires only the minimum number of input images. However, ignorance of spatial variation in land cover between pixels is a common issue in existing SU methods. For example, all coarse neighbors in a local window are treated equally in the unmixing model, which is inappropriate. Moreover, the determination of the appropriate number of clusters in the known fine spatial resolution image remains a challenge. In this article, a geographically weighted SU (SU-GW) method was proposed to address the spatial variation in land cover and increase the accuracy of spatiotemporal fusion. SU-GW is a general model suitable for any SU method. Specifically, the existing regularized version and soft classification-based version were extended with the proposed geographically weighted scheme, producing 24 versions (i.e., 12 existing versions were extended to 12 corresponding geographically weighted versions) for SU. Furthermore, the cluster validity index of Xie and Beni (XB) was introduced to determine automatically the number of clusters. A systematic comparison between the experimental results of the 24 versions indicated that SU-GW was effective in increasing the prediction accuracy. Importantly, all 12 existing methods were enhanced by integrating the SU-GW scheme. Moreover, the identified most accurate SU-GW enhanced version was demonstrated to outperform two prevailing spatiotemporal fusion approaches in a benchmark comparison. Therefore, it can be concluded that SU-GW provides a general solution for enhancing spatiotemporal fusion, which can be used to update existing methods and future potential versions.",
keywords = "Artificial satellites, Data integration, Earth, Geographical weighting (GW), image fusion, Remote sensing, Spatial resolution, spatial unmixing (SU), spatiotemporal fusion., Spatiotemporal phenomena, Uncertainty, Image fusion, Geographical weighting, Remote-sensing, Spatial and temporal resolutions, Spatial unmixing, Spatio-temporal fusions, Spatiotemporal fusion., Spatiotemporal phenomenon, Unmixing, Image resolution",
author = "K. Peng and Q. Wang and Y. Tang and X. Tong and P.M. Atkinson",
note = "{\textcopyright}2021 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 = "2022",
month = jan,
day = "31",
doi = "10.1109/TGRS.2021.3115136",
language = "English",
volume = "60",
journal = "IEEE Transactions on Geoscience and Remote Sensing",
issn = "0196-2892",
publisher = "IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC",

}

RIS

TY - JOUR

T1 - Geographically Weighted Spatial Unmixing for Spatiotemporal Fusion

AU - Peng, K.

AU - Wang, Q.

AU - Tang, Y.

AU - Tong, X.

AU - Atkinson, P.M.

N1 - ©2021 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 - 2022/1/31

Y1 - 2022/1/31

N2 - Spatiotemporal fusion is a technique applied to create images with both fine spatial and temporal resolutions by blending images with different spatial and temporal resolutions. Spatial unmixing (SU) is a widely used approach for spatiotemporal fusion, which requires only the minimum number of input images. However, ignorance of spatial variation in land cover between pixels is a common issue in existing SU methods. For example, all coarse neighbors in a local window are treated equally in the unmixing model, which is inappropriate. Moreover, the determination of the appropriate number of clusters in the known fine spatial resolution image remains a challenge. In this article, a geographically weighted SU (SU-GW) method was proposed to address the spatial variation in land cover and increase the accuracy of spatiotemporal fusion. SU-GW is a general model suitable for any SU method. Specifically, the existing regularized version and soft classification-based version were extended with the proposed geographically weighted scheme, producing 24 versions (i.e., 12 existing versions were extended to 12 corresponding geographically weighted versions) for SU. Furthermore, the cluster validity index of Xie and Beni (XB) was introduced to determine automatically the number of clusters. A systematic comparison between the experimental results of the 24 versions indicated that SU-GW was effective in increasing the prediction accuracy. Importantly, all 12 existing methods were enhanced by integrating the SU-GW scheme. Moreover, the identified most accurate SU-GW enhanced version was demonstrated to outperform two prevailing spatiotemporal fusion approaches in a benchmark comparison. Therefore, it can be concluded that SU-GW provides a general solution for enhancing spatiotemporal fusion, which can be used to update existing methods and future potential versions.

AB - Spatiotemporal fusion is a technique applied to create images with both fine spatial and temporal resolutions by blending images with different spatial and temporal resolutions. Spatial unmixing (SU) is a widely used approach for spatiotemporal fusion, which requires only the minimum number of input images. However, ignorance of spatial variation in land cover between pixels is a common issue in existing SU methods. For example, all coarse neighbors in a local window are treated equally in the unmixing model, which is inappropriate. Moreover, the determination of the appropriate number of clusters in the known fine spatial resolution image remains a challenge. In this article, a geographically weighted SU (SU-GW) method was proposed to address the spatial variation in land cover and increase the accuracy of spatiotemporal fusion. SU-GW is a general model suitable for any SU method. Specifically, the existing regularized version and soft classification-based version were extended with the proposed geographically weighted scheme, producing 24 versions (i.e., 12 existing versions were extended to 12 corresponding geographically weighted versions) for SU. Furthermore, the cluster validity index of Xie and Beni (XB) was introduced to determine automatically the number of clusters. A systematic comparison between the experimental results of the 24 versions indicated that SU-GW was effective in increasing the prediction accuracy. Importantly, all 12 existing methods were enhanced by integrating the SU-GW scheme. Moreover, the identified most accurate SU-GW enhanced version was demonstrated to outperform two prevailing spatiotemporal fusion approaches in a benchmark comparison. Therefore, it can be concluded that SU-GW provides a general solution for enhancing spatiotemporal fusion, which can be used to update existing methods and future potential versions.

KW - Artificial satellites

KW - Data integration

KW - Earth

KW - Geographical weighting (GW)

KW - image fusion

KW - Remote sensing

KW - Spatial resolution

KW - spatial unmixing (SU)

KW - spatiotemporal fusion.

KW - Spatiotemporal phenomena

KW - Uncertainty

KW - Image fusion

KW - Geographical weighting

KW - Remote-sensing

KW - Spatial and temporal resolutions

KW - Spatial unmixing

KW - Spatio-temporal fusions

KW - Spatiotemporal fusion.

KW - Spatiotemporal phenomenon

KW - Unmixing

KW - Image resolution

U2 - 10.1109/TGRS.2021.3115136

DO - 10.1109/TGRS.2021.3115136

M3 - Journal article

VL - 60

JO - IEEE Transactions on Geoscience and Remote Sensing

JF - IEEE Transactions on Geoscience and Remote Sensing

SN - 0196-2892

M1 - 5404217

ER -