research-article

Geographical Hidden Markov Tree for Flood Extent Mapping

Authors:

Arpan Man SainjuAuthors Info & Claims

KDD '18: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining

Pages 2545 - 2554

https://doi.org/10.1145/3219819.3220053

Published: 19 July 2018 Publication History

Abstract

Flood extent mapping plays a crucial role in disaster management and national water forecasting. Unfortunately, traditional classification methods are often hampered by the existence of noise, obstacles and heterogeneity in spectral features as well as implicit anisotropic spatial dependency across class labels. In this paper, we propose geographical hidden Markov tree, a probabilistic graphical model that generalizes the common hidden Markov model from a one dimensional sequence to a two dimensional map. Partial order class dependency is incorporated in the hidden class layer with a reverse tree structure. We also investigate computational algorithms for reverse tree construction, model parameter learning and class inference. Extensive evaluations on both synthetic and real world datasets show that proposed model outperforms multiple baselines in flood mapping, and our algorithms are scalable on large data sizes.

Supplementary Material

MP4 File (jiang_geographical_markov.mp4)

Download
414.22 MB

References

[1]

Jón Atli Benediktsson, Jón Aevar Palmason, and Johannes R Sveinsson . 2005. Classification of hyperspectral data from urban areas based on extended morphological profiles. IEEE Transactions on Geoscience and Remote Sensing Vol. 43, 3 (2005), 480--491.

[2]

PA Brivio, R Colombo, M Maggi, and R Tomasoni . 2002. Integration of remote sensing data and GIS for accurate mapping of flooded areas. International Journal of Remote Sensing Vol. 23, 3 (2002), 429--441.

[3]

Raymond H Chan, Chung-Wa Ho, and Mila Nikolova . 2005. Salt-and-pepper noise removal by median-type noise detectors and detail-preserving regularization. Image Processing, IEEE Transactions on Vol. 14, 10 (2005), 1479--1485.

Digital Library

[4]

Don Cline . 2009. Integrated Water Resources Science and Services: an Integrated and Adaptive Roadmap for Operational Implementation. Technical Report. National Oceanic and Atmospheric Administration.

[5]

Matthew S Crouse, Robert D Nowak, and Richard G Baraniuk . 1998. Wavelet-based statistical signal processing using hidden Markov models. IEEE Transactions on signal processing Vol. 46, 4 (1998), 886--902.

Digital Library

[6]

S Esakkirajan, T Veerakumar, Adabala N Subramanyam, and CH PremChand . 2011. Removal of high density salt and pepper noise through modified decision based unsymmetric trimmed median filter. Signal Processing Letters, IEEE Vol. 18, 5 (2011), 287--290.

[7]

GJ Hay and G Castilla . 2008. Geographic Object-Based Image Analysis (GEOBIA): A new name for a new discipline. In Object-based image analysis. Springer, 75--89.

[8]

Xiaowei Jia, Ankush Khandelwal, Guruprasad Nayak, James Gerber, Kimberly Carlson, Paul West, and Vipin Kumar . 2017. Incremental dual-memory lstm in land cover prediction Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 867--876.

Digital Library

[9]

Zhe Jiang, Shashi Shekhar, Xun Zhou, Joseph Knight, and Jennifer Corcoran . 2015. Focal-test-based spatial decision tree learning. IEEE Transactions on Knowledge and Data Engineering Vol. 27, 6 (2015), 1547--1559.

Digital Library

[10]

T. Joachims . 1999. Making large-Scale SVM Learning Practical. In Advances in Kernel Methods - Support Vector Learning, bibfieldeditorB. Schölkopf, C. Burges, and A. Smola (Eds.). MIT Press, Cambridge, MA, Chapter 11, 169--184.

Digital Library

[11]

Ankush Khandelwal, Varun Mithal, and Vipin Kumar . 2015. Post Classification Label Refinement Using Implicit Ordering Constraint Among Data Instances 2015 IEEE International Conference on Data Mining, ICDM 2015, Atlantic City, NJ, USA, November 14--17, 2015. 799--804.

Digital Library

[12]

Frank R Kschischang, Brendan J Frey, and H-A Loeliger . 2001. Factor graphs and the sum-product algorithm. IEEE Transactions on information theory Vol. 47, 2 (2001), 498--519.

Digital Library

[13]

Stan Z Li . 2009. Markov random field modeling in image analysis. Springer Science & Business Media.

[14]

National Oceanic and Atmospheric Administration . {n. d.}. Data and Imagery from NOAA's National Geodetic Survey. https://www.ngs.noaa.gov. (. {n. d.}).

[15]

National Oceanic and Atmospheric Administration . 2018. National Water Model: Improving NOAA's Water Prediction Services. http://water.noaa.gov/documents/wrn-national-water-model.pdf. (2018).

[16]

NCSU Libraries . 2018. LIDAR Based Elevation Data for North Carolina. https://www.lib.ncsu.edu/gis/elevation. (2018).

[17]

Anne Puissant, Jacky Hirsch, and Christiane Weber . 2005. The utility of texture analysis to improve per-pixel classification for high to very high spatial resolution imagery. International Journal of Remote Sensing Vol. 26, 4 (2005), 733--745.

[18]

Lawrence R Rabiner . 1989. A tutorial on hidden Markov models and selected applications in speech recognition. Proc. IEEE Vol. 77, 2 (1989), 257--286.

[19]

O Ronen, JR Rohlicek, and M Ostendorf . 1995. Parameter estimation of dependence tree models using the EM algorithm. IEEE Signal Processing Letters Vol. 2, 8 (1995), 157--159.

[20]

Richard Szeliski, Ramin Zabih, Daniel Scharstein, Olga Veksler, Vladimir Kolmogorov, Aseem Agarwala, Marshall Tappen, and Carsten Rother . 2006. A comparative study of energy minimization methods for markov random fields European conference on computer vision. Springer, 16--29.

Digital Library

[21]

Yuliya Tarabalka, Jón Atli Benediktsson, and Jocelyn Chanussot . 2009. Spectral--spatial classification of hyperspectral imagery based on partitional clustering techniques. IEEE Transactions on Geoscience and Remote Sensing Vol. 47, 8 (2009), 2973--2987.

[22]

The Middlebury Computer Vision Pages . 2018. C

[23]

Source Code of MRF. http://vision.middlebury.edu/MRF/code/. (2018).

[24]

Liguo Wang, Siyuan Hao, Qunming Wang, and Ying Wang . 2014. Semi-supervised classification for hyperspectral imagery based on spatial-spectral label propagation. ISPRS Journal of Photogrammetry and Remote Sensing Vol. 97 (2014), 123--137.

[25]

Xie, Miao and Jiang, Zhe and Sainju, Arpan Man . 2018. Geographical Hidden Markov Tree for Flood Extent Mapping (With Proof Appendix). https://arxiv.org/abs/1805.09757. (2018).

[26]

Liangpei Zhang, Lefei Zhang, and Bo Du . 2016. Deep learning for remote sensing data: A technical tutorial on the state of the art. IEEE Geoscience and Remote Sensing Magazine Vol. 4, 2 (2016), 22--40.

[27]

Xiaojin Zhu . 2005. Semi-supervised learning literature survey. (2005).

[28]

Xiaojin Zhu and Zoubin Ghahramani . 2002. Learning from labeled and unlabeled data with label propagation. (2002).

Cited By

Farhadi HEbadi HKiani AAsgary A(2024)Near Real-Time Flood Monitoring Using Multi-Sensor Optical Imagery and Machine Learning by GEE: An Automatic Feature-Based Multi-Class Classification ApproachRemote Sensing10.3390/rs1623445416:23(4454)Online publication date: 27-Nov-2024
https://doi.org/10.3390/rs16234454
Sami MYan DAdhikari SYuan LHan JJiang ZKhalil JZhou YLarson K(2024)EvaNetProceedings of the Thirty-Third International Joint Conference on Artificial Intelligence10.24963/ijcai.2024/133(1200-1208)Online publication date: 3-Aug-2024
https://dl.acm.org/doi/10.24963/ijcai.2024/133
Adhikari SYan DJiang ZHan JXu ZZhang YSainju AZhou Y(2024)Scaling Terrain-Aware Spatial Machine Learning for Flood Mapping on Large Scale Earth Imagery DataACM Transactions on Spatial Algorithms and Systems10.1145/3703157Online publication date: 5-Nov-2024
https://doi.org/10.1145/3703157
Show More Cited By

Index Terms

Geographical Hidden Markov Tree for Flood Extent Mapping

Recommendations

Hidden Markov Contour Tree: A Spatial Structured Model for Hydrological Applications
KDD '19: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining

Spatial structured models are predictive models that capture dependency structure between samples based on their locations in the space. Learning such models plays an important role in many geoscience applications such as water surface mapping, but it ...
Mixture-State Document Segmentation Using Wavelet-Domain Hidden Markov Tree Models
WAA '01: Proceedings of the Second International Conference on Wavelet Analysis and Its Applications

In this paper we introduce a mixture-state document segmentation method based on wavelet and the hidden Markov tree (HMT) models. First we propose a three-state HMT segmentation method that is similar to those in the reference [1]. Then through ...
Coding with partially hidden Markov models
DCC '95: Proceedings of the Conference on Data Compression

Partially hidden Markov models (PHMM) are introduced. They are a variation of the hidden Markov models (HMM) combining the power of explicit conditioning on past observations and the power of using hidden states. (P)HMM may be combined with arithmetic ...

Comments

Information & Contributors

Information

Published In

cover image ACM Other conferences

KDD '18: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining

July 2018

2925 pages

ISBN:9781450355520

DOI:10.1145/3219819

General Chairs:
Yike Guo
Imperial College London
,
Faisal Farooq
IBM

Copyright © 2018 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 19 July 2018

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

KDD '18

Sponsor:

KDD '18: The 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

August 19 - 23, 2018

London, United Kingdom

Acceptance Rates

KDD '18 Paper Acceptance Rate 107 of 983 submissions, 11%;

Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

31
Total Citations
View Citations
556
Total Downloads

Downloads (Last 12 months)24
Downloads (Last 6 weeks)4

Reflects downloads up to 01 Mar 2025

Other Metrics

View Author Metrics

Citations

Cited By

Farhadi HEbadi HKiani AAsgary A(2024)Near Real-Time Flood Monitoring Using Multi-Sensor Optical Imagery and Machine Learning by GEE: An Automatic Feature-Based Multi-Class Classification ApproachRemote Sensing10.3390/rs1623445416:23(4454)Online publication date: 27-Nov-2024
https://doi.org/10.3390/rs16234454
Sami MYan DAdhikari SYuan LHan JJiang ZKhalil JZhou YLarson K(2024)EvaNetProceedings of the Thirty-Third International Joint Conference on Artificial Intelligence10.24963/ijcai.2024/133(1200-1208)Online publication date: 3-Aug-2024
https://dl.acm.org/doi/10.24963/ijcai.2024/133
Adhikari SYan DJiang ZHan JXu ZZhang YSainju AZhou Y(2024)Scaling Terrain-Aware Spatial Machine Learning for Flood Mapping on Large Scale Earth Imagery DataACM Transactions on Spatial Algorithms and Systems10.1145/3703157Online publication date: 5-Nov-2024
https://doi.org/10.1145/3703157
Vongkusolkit JPeng BWu MHuang QAndresen C(2023)Near Real-Time Flood Mapping with Weakly Supervised Machine LearningRemote Sensing10.3390/rs1513326315:13(3263)Online publication date: 25-Jun-2023
https://doi.org/10.3390/rs15133263
Xu ZXiao THe WWang YJiang ZRenz MNascimento M(2023)Spatial Knowledge-Infused Hierarchical Learning: An Application in Flood Mapping on Earth ImageryProceedings of the 31st ACM International Conference on Advances in Geographic Information Systems10.1145/3589132.3625591(1-10)Online publication date: 13-Nov-2023
https://dl.acm.org/doi/10.1145/3589132.3625591
Gao GBao ZCao JQin ASellis T(2022)Location-Centered House Price Prediction: A Multi-Task Learning ApproachACM Transactions on Intelligent Systems and Technology10.1145/350180613:2(1-25)Online publication date: 5-Jan-2022
https://dl.acm.org/doi/10.1145/3501806
Wang LYu ZGuo BYang DMa LLiu ZXiong F(2022)Data-driven Targeted Advertising Recommendation System for Outdoor BillboardACM Transactions on Intelligent Systems and Technology10.1145/349515913:2(1-23)Online publication date: 5-Jan-2022
https://dl.acm.org/doi/10.1145/3495159
Lu BGan XJin HFu LWang XZhang H(2022)Make More Connections: Urban Traffic Flow Forecasting with Spatiotemporal Adaptive Gated Graph Convolution NetworkACM Transactions on Intelligent Systems and Technology10.1145/348890213:2(1-25)Online publication date: 5-Jan-2022
https://dl.acm.org/doi/10.1145/3488902
Bao HZhou XXie YZhang YLi Y(2022)COVID-GAN+: Estimating Human Mobility Responses to COVID-19 through Spatio-temporal Generative Adversarial Networks with Enhanced FeaturesACM Transactions on Intelligent Systems and Technology10.1145/348161713:2(1-23)Online publication date: 5-Jan-2022
https://dl.acm.org/doi/10.1145/3481617
He WSainju AJiang ZYan DZhou Y(2022)Earth Imagery Segmentation on Terrain Surface with Limited Training Labels: A Semi-supervised Approach based on Physics-Guided Graph Co-TrainingACM Transactions on Intelligent Systems and Technology10.1145/348104313:2(1-22)Online publication date: 5-Jan-2022
https://dl.acm.org/doi/10.1145/3481043
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Figures

Tables

Media

View Table of Conten