Change detection from temporal sequences of class labels: Application to land cover change mapping

Varun Mithal; Ankush Khandelwal; Shyam Boriah; Karsten Steinhaeuser; Vipin Kumar

doi:10.1137/1.9781611972832.72

Change detection from temporal sequences of class labels: Application to land cover change mapping

Varun Mithal, Ankush Khandelwal, Shyam Boriah, Karsten Steinhaeuser, Vipin Kumar

Computer Science and Engineering

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

10 Scopus citations

Abstract

Mapping land cover change is an important problem for the scientific community as well as policy makers. Traditionally, bi-temporal classification of satellite data is used to identify areas of land cover change. However, these classification products often have errors due to classifier inaccuracy or poor data, which poses significant issues when using them for land cover change detection. In this paper, we propose a generative model for land cover label sequences and use it to reassign a more accurate sequence of land cover labels to every pixel. Empirical evaluation on real and synthetic data suggests that the proposed approach is effective in capturing the characteristics of land cover classification and change processes, and produces significantly improved classification and change detection products.

Original language	English (US)
Title of host publication	Proceedings of the 2013 SIAM International Conference on Data Mining, SDM 2013
Editors	Joydeep Ghosh, Zoran Obradovic, Jennifer Dy, Zhi-Hua Zhou, Chandrika Kamath, Srinivasan Parthasarathy
Publisher	Siam Society
Pages	650-658
Number of pages	9
ISBN (Electronic)	9781611972627
DOIs	https://doi.org/10.1137/1.9781611972832.72
State	Published - 2013
Event	SIAM International Conference on Data Mining, SDM 2013 - Austin, United States Duration: May 2 2013 → May 4 2013

Publication series

Name	Proceedings of the 2013 SIAM International Conference on Data Mining, SDM 2013

Other

Other	SIAM International Conference on Data Mining, SDM 2013
Country/Territory	United States
City	Austin
Period	5/2/13 → 5/4/13

Bibliographical note

Publisher Copyright:
Copyright © SIAM.

Access

10.1137/1.9781611972832.72

OpenUrl availability

Full text

Cite this

Mithal, V., Khandelwal, A., Boriah, S., Steinhaeuser, K., & Kumar, V. (2013). Change detection from temporal sequences of class labels: Application to land cover change mapping. In J. Ghosh, Z. Obradovic, J. Dy, Z.-H. Zhou, C. Kamath, & S. Parthasarathy (Eds.), Proceedings of the 2013 SIAM International Conference on Data Mining, SDM 2013 (pp. 650-658). (Proceedings of the 2013 SIAM International Conference on Data Mining, SDM 2013). Siam Society. https://doi.org/10.1137/1.9781611972832.72

Change detection from temporal sequences of class labels: Application to land cover change mapping. / Mithal, Varun; Khandelwal, Ankush; Boriah, Shyam et al.
Proceedings of the 2013 SIAM International Conference on Data Mining, SDM 2013. ed. / Joydeep Ghosh; Zoran Obradovic; Jennifer Dy; Zhi-Hua Zhou; Chandrika Kamath; Srinivasan Parthasarathy. Siam Society, 2013. p. 650-658 (Proceedings of the 2013 SIAM International Conference on Data Mining, SDM 2013).

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

Mithal, V, Khandelwal, A, Boriah, S, Steinhaeuser, K & Kumar, V 2013, Change detection from temporal sequences of class labels: Application to land cover change mapping. in J Ghosh, Z Obradovic, J Dy, Z-H Zhou, C Kamath & S Parthasarathy (eds), Proceedings of the 2013 SIAM International Conference on Data Mining, SDM 2013. Proceedings of the 2013 SIAM International Conference on Data Mining, SDM 2013, Siam Society, pp. 650-658, SIAM International Conference on Data Mining, SDM 2013, Austin, United States, 5/2/13. https://doi.org/10.1137/1.9781611972832.72

Mithal V, Khandelwal A, Boriah S, Steinhaeuser K, Kumar V. Change detection from temporal sequences of class labels: Application to land cover change mapping. In Ghosh J, Obradovic Z, Dy J, Zhou ZH, Kamath C, Parthasarathy S, editors, Proceedings of the 2013 SIAM International Conference on Data Mining, SDM 2013. Siam Society. 2013. p. 650-658. (Proceedings of the 2013 SIAM International Conference on Data Mining, SDM 2013). doi: 10.1137/1.9781611972832.72

Mithal, Varun ; Khandelwal, Ankush ; Boriah, Shyam et al. / Change detection from temporal sequences of class labels : Application to land cover change mapping. Proceedings of the 2013 SIAM International Conference on Data Mining, SDM 2013. editor / Joydeep Ghosh ; Zoran Obradovic ; Jennifer Dy ; Zhi-Hua Zhou ; Chandrika Kamath ; Srinivasan Parthasarathy. Siam Society, 2013. pp. 650-658 (Proceedings of the 2013 SIAM International Conference on Data Mining, SDM 2013).

@inproceedings{6c0db2b420e145258bed45e9cbeb273e,

title = "Change detection from temporal sequences of class labels: Application to land cover change mapping",

abstract = "Mapping land cover change is an important problem for the scientific community as well as policy makers. Traditionally, bi-temporal classification of satellite data is used to identify areas of land cover change. However, these classification products often have errors due to classifier inaccuracy or poor data, which poses significant issues when using them for land cover change detection. In this paper, we propose a generative model for land cover label sequences and use it to reassign a more accurate sequence of land cover labels to every pixel. Empirical evaluation on real and synthetic data suggests that the proposed approach is effective in capturing the characteristics of land cover classification and change processes, and produces significantly improved classification and change detection products.",

author = "Varun Mithal and Ankush Khandelwal and Shyam Boriah and Karsten Steinhaeuser and Vipin Kumar",

note = "Publisher Copyright: Copyright {\textcopyright} SIAM.; SIAM International Conference on Data Mining, SDM 2013 ; Conference date: 02-05-2013 Through 04-05-2013",

year = "2013",

doi = "10.1137/1.9781611972832.72",

language = "English (US)",

series = "Proceedings of the 2013 SIAM International Conference on Data Mining, SDM 2013",

publisher = "Siam Society",

pages = "650--658",

editor = "Joydeep Ghosh and Zoran Obradovic and Jennifer Dy and Zhi-Hua Zhou and Chandrika Kamath and Srinivasan Parthasarathy",

booktitle = "Proceedings of the 2013 SIAM International Conference on Data Mining, SDM 2013",

}

TY - GEN

T1 - Change detection from temporal sequences of class labels

T2 - SIAM International Conference on Data Mining, SDM 2013

AU - Mithal, Varun

AU - Khandelwal, Ankush

AU - Boriah, Shyam

AU - Steinhaeuser, Karsten

AU - Kumar, Vipin

PY - 2013

Y1 - 2013

N2 - Mapping land cover change is an important problem for the scientific community as well as policy makers. Traditionally, bi-temporal classification of satellite data is used to identify areas of land cover change. However, these classification products often have errors due to classifier inaccuracy or poor data, which poses significant issues when using them for land cover change detection. In this paper, we propose a generative model for land cover label sequences and use it to reassign a more accurate sequence of land cover labels to every pixel. Empirical evaluation on real and synthetic data suggests that the proposed approach is effective in capturing the characteristics of land cover classification and change processes, and produces significantly improved classification and change detection products.

AB - Mapping land cover change is an important problem for the scientific community as well as policy makers. Traditionally, bi-temporal classification of satellite data is used to identify areas of land cover change. However, these classification products often have errors due to classifier inaccuracy or poor data, which poses significant issues when using them for land cover change detection. In this paper, we propose a generative model for land cover label sequences and use it to reassign a more accurate sequence of land cover labels to every pixel. Empirical evaluation on real and synthetic data suggests that the proposed approach is effective in capturing the characteristics of land cover classification and change processes, and produces significantly improved classification and change detection products.

UR - http://www.scopus.com/inward/record.url?scp=84950132379&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84950132379&partnerID=8YFLogxK

U2 - 10.1137/1.9781611972832.72

DO - 10.1137/1.9781611972832.72

M3 - Conference contribution

AN - SCOPUS:84950132379

T3 - Proceedings of the 2013 SIAM International Conference on Data Mining, SDM 2013

SP - 650

EP - 658

BT - Proceedings of the 2013 SIAM International Conference on Data Mining, SDM 2013

A2 - Ghosh, Joydeep

A2 - Obradovic, Zoran

A2 - Dy, Jennifer

A2 - Zhou, Zhi-Hua

A2 - Kamath, Chandrika

A2 - Parthasarathy, Srinivasan

PB - Siam Society

Y2 - 2 May 2013 through 4 May 2013

ER -

Change detection from temporal sequences of class labels: Application to land cover change mapping

Abstract

Publication series

Other

Bibliographical note

Access

OpenUrl availability

Other files and links

Fingerprint

Cite this