Spatio-temporal classification at multiple resolutions using multi-view regularization

Guruprasad Nayak; Rahul Ghosh; Xiaowei Jia; Varun Mithal; Vipin Kumar

doi:10.1109/BigData47090.2019.9006597

Spatio-temporal classification at multiple resolutions using multi-view regularization

Guruprasad Nayak, Rahul Ghosh, Xiaowei Jia, Varun Mithal, Vipin Kumar

Computer Science and Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

In this work, we present a multi-view framework to classify spatio-temporal phenomena at multiple resolutions. This approach utilizes the complementarity of features across different resolutions and improves the corresponding models by enforcing consistency of their predictions on unlabeled data. Unlike traditional multi-view learning problems, the key challenge in our case is that there is a many-to-one correspondence between instances across different resolutions, which needs to be explicitly modeled. Experiments on the real-world application of mapping urban areas using spatial raster data-sets from satellite observations show the benefits of the proposed multi-view framework.

Original language	English (US)
Title of host publication	Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019
Editors	Chaitanya Baru, Jun Huan, Latifur Khan, Xiaohua Tony Hu, Ronay Ak, Yuanyuan Tian, Roger Barga, Carlo Zaniolo, Kisung Lee, Yanfang Fanny Ye
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	4117-4120
Number of pages	4
ISBN (Electronic)	9781728108582
DOIs	https://doi.org/10.1109/BigData47090.2019.9006597
State	Published - Dec 2019
Event	2019 IEEE International Conference on Big Data, Big Data 2019 - Los Angeles, United States Duration: Dec 9 2019 → Dec 12 2019

Publication series

Name	Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019

Conference

Conference	2019 IEEE International Conference on Big Data, Big Data 2019
Country/Territory	United States
City	Los Angeles
Period	12/9/19 → 12/12/19

Bibliographical note

Funding Information:
ACKNOWLEDGEMENT This research was supported by National Science Foundation under Grants IIS-1838159. Access to computing facilities was provided by the University of Minnesota Supercomputing Institute.

Publisher Copyright:
© 2019 IEEE.

Keywords

multi-instance learning
multi-resolution classification
remote sensing

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access

10.1109/BigData47090.2019.9006597

OpenUrl availability

Full text

Cite this

Nayak, G., Ghosh, R., Jia, X., Mithal, V., & Kumar, V. (2019). Spatio-temporal classification at multiple resolutions using multi-view regularization. In C. Baru, J. Huan, L. Khan, X. T. Hu, R. Ak, Y. Tian, R. Barga, C. Zaniolo, K. Lee, & Y. F. Ye (Eds.), Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019 (pp. 4117-4120). Article 9006597 (Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BigData47090.2019.9006597

Spatio-temporal classification at multiple resolutions using multi-view regularization. / Nayak, Guruprasad; Ghosh, Rahul; Jia, Xiaowei et al.
Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019. ed. / Chaitanya Baru; Jun Huan; Latifur Khan; Xiaohua Tony Hu; Ronay Ak; Yuanyuan Tian; Roger Barga; Carlo Zaniolo; Kisung Lee; Yanfang Fanny Ye. Institute of Electrical and Electronics Engineers Inc., 2019. p. 4117-4120 9006597 (Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Nayak, G, Ghosh, R, Jia, X, Mithal, V & Kumar, V 2019, Spatio-temporal classification at multiple resolutions using multi-view regularization. in C Baru, J Huan, L Khan, XT Hu, R Ak, Y Tian, R Barga, C Zaniolo, K Lee & YF Ye (eds), Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019., 9006597, Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019, Institute of Electrical and Electronics Engineers Inc., pp. 4117-4120, 2019 IEEE International Conference on Big Data, Big Data 2019, Los Angeles, United States, 12/9/19. https://doi.org/10.1109/BigData47090.2019.9006597

Nayak G, Ghosh R, Jia X, Mithal V, Kumar V. Spatio-temporal classification at multiple resolutions using multi-view regularization. In Baru C, Huan J, Khan L, Hu XT, Ak R, Tian Y, Barga R, Zaniolo C, Lee K, Ye YF, editors, Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019. Institute of Electrical and Electronics Engineers Inc. 2019. p. 4117-4120. 9006597. (Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019). doi: 10.1109/BigData47090.2019.9006597

Nayak, Guruprasad ; Ghosh, Rahul ; Jia, Xiaowei et al. / Spatio-temporal classification at multiple resolutions using multi-view regularization. Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019. editor / Chaitanya Baru ; Jun Huan ; Latifur Khan ; Xiaohua Tony Hu ; Ronay Ak ; Yuanyuan Tian ; Roger Barga ; Carlo Zaniolo ; Kisung Lee ; Yanfang Fanny Ye. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 4117-4120 (Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019).

@inproceedings{3fae8f2bfcd243bfadf93e4c40c04c52,

title = "Spatio-temporal classification at multiple resolutions using multi-view regularization",

abstract = "In this work, we present a multi-view framework to classify spatio-temporal phenomena at multiple resolutions. This approach utilizes the complementarity of features across different resolutions and improves the corresponding models by enforcing consistency of their predictions on unlabeled data. Unlike traditional multi-view learning problems, the key challenge in our case is that there is a many-to-one correspondence between instances across different resolutions, which needs to be explicitly modeled. Experiments on the real-world application of mapping urban areas using spatial raster data-sets from satellite observations show the benefits of the proposed multi-view framework.",

keywords = "multi-instance learning, multi-resolution classification, remote sensing",

author = "Guruprasad Nayak and Rahul Ghosh and Xiaowei Jia and Varun Mithal and Vipin Kumar",

note = "Funding Information: ACKNOWLEDGEMENT This research was supported by National Science Foundation under Grants IIS-1838159. Access to computing facilities was provided by the University of Minnesota Supercomputing Institute. Publisher Copyright: {\textcopyright} 2019 IEEE.; 2019 IEEE International Conference on Big Data, Big Data 2019 ; Conference date: 09-12-2019 Through 12-12-2019",

year = "2019",

month = dec,

doi = "10.1109/BigData47090.2019.9006597",

language = "English (US)",

series = "Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "4117--4120",

editor = "Chaitanya Baru and Jun Huan and Latifur Khan and Hu, {Xiaohua Tony} and Ronay Ak and Yuanyuan Tian and Roger Barga and Carlo Zaniolo and Kisung Lee and Ye, {Yanfang Fanny}",

booktitle = "Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019",

}

TY - GEN

T1 - Spatio-temporal classification at multiple resolutions using multi-view regularization

AU - Nayak, Guruprasad

AU - Ghosh, Rahul

AU - Jia, Xiaowei

AU - Mithal, Varun

AU - Kumar, Vipin

N1 - Funding Information: ACKNOWLEDGEMENT This research was supported by National Science Foundation under Grants IIS-1838159. Access to computing facilities was provided by the University of Minnesota Supercomputing Institute. Publisher Copyright: © 2019 IEEE.

PY - 2019/12

Y1 - 2019/12

N2 - In this work, we present a multi-view framework to classify spatio-temporal phenomena at multiple resolutions. This approach utilizes the complementarity of features across different resolutions and improves the corresponding models by enforcing consistency of their predictions on unlabeled data. Unlike traditional multi-view learning problems, the key challenge in our case is that there is a many-to-one correspondence between instances across different resolutions, which needs to be explicitly modeled. Experiments on the real-world application of mapping urban areas using spatial raster data-sets from satellite observations show the benefits of the proposed multi-view framework.

AB - In this work, we present a multi-view framework to classify spatio-temporal phenomena at multiple resolutions. This approach utilizes the complementarity of features across different resolutions and improves the corresponding models by enforcing consistency of their predictions on unlabeled data. Unlike traditional multi-view learning problems, the key challenge in our case is that there is a many-to-one correspondence between instances across different resolutions, which needs to be explicitly modeled. Experiments on the real-world application of mapping urban areas using spatial raster data-sets from satellite observations show the benefits of the proposed multi-view framework.

KW - multi-instance learning

KW - multi-resolution classification

KW - remote sensing

UR - http://www.scopus.com/inward/record.url?scp=85081384420&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85081384420&partnerID=8YFLogxK

U2 - 10.1109/BigData47090.2019.9006597

DO - 10.1109/BigData47090.2019.9006597

M3 - Conference contribution

AN - SCOPUS:85081384420

T3 - Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019

SP - 4117

EP - 4120

BT - Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019

A2 - Baru, Chaitanya

A2 - Huan, Jun

A2 - Khan, Latifur

A2 - Hu, Xiaohua Tony

A2 - Ak, Ronay

A2 - Tian, Yuanyuan

A2 - Barga, Roger

A2 - Zaniolo, Carlo

A2 - Lee, Kisung

A2 - Ye, Yanfang Fanny

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2019 IEEE International Conference on Big Data, Big Data 2019

Y2 - 9 December 2019 through 12 December 2019

ER -

Spatio-temporal classification at multiple resolutions using multi-view regularization

Abstract

Publication series

Conference

Bibliographical note

Keywords

UN SDGs

Access

OpenUrl availability

Other files and links

Fingerprint

Cite this