Home > Research > Publications & Outputs > Spatiotemporal subpixel mapping of time-series ...

Electronic data

  • Spatiotemporal subpixel mappping oftime-series images_final version

    Rights statement: ©2016 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.

    Accepted author manuscript, 1.3 MB, PDF document

    Available under license: CC BY-NC: Creative Commons Attribution-NonCommercial 4.0 International License

Links

Text available via DOI:

View graph of relations

Spatiotemporal subpixel mapping of time-series images

Research output: Contribution to journalJournal articlepeer-review

Published

Standard

Spatiotemporal subpixel mapping of time-series images. / Wang, Qunming; Shi, Wenzhong; Atkinson, Peter Michael.

In: IEEE Transactions on Geoscience and Remote Sensing, Vol. 54, No. 9, 09.2016, p. 5397-5411.

Research output: Contribution to journalJournal articlepeer-review

Harvard

Wang, Q, Shi, W & Atkinson, PM 2016, 'Spatiotemporal subpixel mapping of time-series images', IEEE Transactions on Geoscience and Remote Sensing, vol. 54, no. 9, pp. 5397-5411. https://doi.org/10.1109/TGRS.2016.2562178

APA

Wang, Q., Shi, W., & Atkinson, P. M. (2016). Spatiotemporal subpixel mapping of time-series images. IEEE Transactions on Geoscience and Remote Sensing, 54(9), 5397-5411. https://doi.org/10.1109/TGRS.2016.2562178

Vancouver

Wang Q, Shi W, Atkinson PM. Spatiotemporal subpixel mapping of time-series images. IEEE Transactions on Geoscience and Remote Sensing. 2016 Sep;54(9):5397-5411. https://doi.org/10.1109/TGRS.2016.2562178

Author

Wang, Qunming ; Shi, Wenzhong ; Atkinson, Peter Michael. / Spatiotemporal subpixel mapping of time-series images. In: IEEE Transactions on Geoscience and Remote Sensing. 2016 ; Vol. 54, No. 9. pp. 5397-5411.

Bibtex

@article{1ea1f6c53e1a49e6adee3a4f7894c5df,
title = "Spatiotemporal subpixel mapping of time-series images",
abstract = "Land cover/land use (LCLU) information extraction from multitemporal sequences of remote sensing imagery is becoming increasingly important. Mixed pixels are a common problem in Landsat and MODIS images that are used widely for LCLU monitoring. Recently developed subpixel mapping (SPM) techniques can extract LCLU information at the subpixel level by dividing mixed pixels into subpixels to which hard classes are then allocated. However, SPM has rarely been studied for time-series images (TSIs). In this paper, a spatiotemporal SPM approach was proposed for SPM of TSIs. In contrast to conventional spatial dependence-based SPM methods, the proposed approach considers simultaneously spatial and temporal dependences, with the former considering the correlation of subpixel classes within each image and the latter considering the correlation of subpixel classes between images in a temporal sequence. The proposed approach was developed assuming the availability of one fine spatial resolution map which exists among the TSIs. The SPM of TSIs is formulated as a constrained optimization problem. Under the coherence constraint imposed by the coarse LCLU proportions, the objective is to maximize the spatiotemporal dependence, which is defined by blending both spatial and temporal dependences. Experiments on three data sets showed that the proposed approach can provide more accurate subpixel resolution TSIs than conventional SPM methods. The SPM results obtained from the TSIs provide an excellent opportunity for LCLU dynamic monitoring and change detection at a finer spatial resolution than the available coarse spatial resolution TSIs.",
author = "Qunming Wang and Wenzhong Shi and Atkinson, {Peter Michael}",
note = "{\textcopyright}2016 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 = "2016",
month = sep,
doi = "10.1109/TGRS.2016.2562178",
language = "English",
volume = "54",
pages = "5397--5411",
journal = "IEEE Transactions on Geoscience and Remote Sensing",
issn = "0196-2892",
publisher = "IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC",
number = "9",

}

RIS

TY - JOUR

T1 - Spatiotemporal subpixel mapping of time-series images

AU - Wang, Qunming

AU - Shi, Wenzhong

AU - Atkinson, Peter Michael

N1 - ©2016 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 - 2016/9

Y1 - 2016/9

N2 - Land cover/land use (LCLU) information extraction from multitemporal sequences of remote sensing imagery is becoming increasingly important. Mixed pixels are a common problem in Landsat and MODIS images that are used widely for LCLU monitoring. Recently developed subpixel mapping (SPM) techniques can extract LCLU information at the subpixel level by dividing mixed pixels into subpixels to which hard classes are then allocated. However, SPM has rarely been studied for time-series images (TSIs). In this paper, a spatiotemporal SPM approach was proposed for SPM of TSIs. In contrast to conventional spatial dependence-based SPM methods, the proposed approach considers simultaneously spatial and temporal dependences, with the former considering the correlation of subpixel classes within each image and the latter considering the correlation of subpixel classes between images in a temporal sequence. The proposed approach was developed assuming the availability of one fine spatial resolution map which exists among the TSIs. The SPM of TSIs is formulated as a constrained optimization problem. Under the coherence constraint imposed by the coarse LCLU proportions, the objective is to maximize the spatiotemporal dependence, which is defined by blending both spatial and temporal dependences. Experiments on three data sets showed that the proposed approach can provide more accurate subpixel resolution TSIs than conventional SPM methods. The SPM results obtained from the TSIs provide an excellent opportunity for LCLU dynamic monitoring and change detection at a finer spatial resolution than the available coarse spatial resolution TSIs.

AB - Land cover/land use (LCLU) information extraction from multitemporal sequences of remote sensing imagery is becoming increasingly important. Mixed pixels are a common problem in Landsat and MODIS images that are used widely for LCLU monitoring. Recently developed subpixel mapping (SPM) techniques can extract LCLU information at the subpixel level by dividing mixed pixels into subpixels to which hard classes are then allocated. However, SPM has rarely been studied for time-series images (TSIs). In this paper, a spatiotemporal SPM approach was proposed for SPM of TSIs. In contrast to conventional spatial dependence-based SPM methods, the proposed approach considers simultaneously spatial and temporal dependences, with the former considering the correlation of subpixel classes within each image and the latter considering the correlation of subpixel classes between images in a temporal sequence. The proposed approach was developed assuming the availability of one fine spatial resolution map which exists among the TSIs. The SPM of TSIs is formulated as a constrained optimization problem. Under the coherence constraint imposed by the coarse LCLU proportions, the objective is to maximize the spatiotemporal dependence, which is defined by blending both spatial and temporal dependences. Experiments on three data sets showed that the proposed approach can provide more accurate subpixel resolution TSIs than conventional SPM methods. The SPM results obtained from the TSIs provide an excellent opportunity for LCLU dynamic monitoring and change detection at a finer spatial resolution than the available coarse spatial resolution TSIs.

U2 - 10.1109/TGRS.2016.2562178

DO - 10.1109/TGRS.2016.2562178

M3 - Journal article

VL - 54

SP - 5397

EP - 5411

JO - IEEE Transactions on Geoscience and Remote Sensing

JF - IEEE Transactions on Geoscience and Remote Sensing

SN - 0196-2892

IS - 9

ER -