research-article

Automatic Classification of Glaciers from Sentinel-2 Imagery Using A Novel Deep Learning Model

Authors:

Rui WuAuthors Info & Claims

ICAIP '19: Proceedings of the 2019 3rd International Conference on Advances in Image Processing

Pages 155 - 159

https://doi.org/10.1145/3373419.3373460

Published: 24 January 2020 Publication History

Abstract

The Sentinel-2 imagery provides accessible multispectral imagery, allowing better operation monitoring of glacier for climate change research, sea level rise and human life. Nevertheless, automatic glacial classification from Sentinel-2 is a challenging due to factors such as complex environment, different resolution bands and noisy or correlation in the spectral or spatial domain. In this paper, we propose an automatic glacier discrimination approach named MSSUnet to address several key research issues. First, a spatial-spectral module is used to adaptively learning the feature from different spectral band and neighboring pixels, which can better learn spatial-spectral features and reduce the impact of noise. Second, a band fusion method is applied to achieve fusion of different resolution bands in Sentinel-2 and reduce the interference of additional information. Furthermore, the proposed MSSUNet is compared with several existing neural networks on Sentinel-2 imagery to justify the advantage and improvement of the proposed approach. Experimental results show the improved performance of our proposed network over the other approaches.

References

[1]

Reznichenko, N., Davies, T., Shulmeister, J., & Mcsaveney, M. (2010). Effects of debris on ice-surface melting rates: an experimental study. Journal of Glaciology, 56(197), 384--394.

[2]

Meier, M. F., Dyurgerov, M. B., Rick, U. K., O"Neel, S., Pfeffer, W. T., & Anderson, R. S., et al. (2007). Glaciers dominate eustatic sea-level rise in the 21st century. Science, 317(5841), 1064--1067.

[3]

Raper, S. C. B., & Braithwaite, R. J. (2006). Low sea level rise projections from mountain glaciers and icecaps under global warming. Nature, 439(7074), 311--313.

[4]

Gardner, A. S., Moholdt, G., Wouters, B., Wolken, G. J., Burgess, D. O., & Sharp, M. J., et al. (2011). Sharply increased mass loss from glaciers and ice caps in the canadian arctic archipelago. NATURE, 473(7347), 357--360.

[5]

Radic, V., & Hock, R. (2011). Regionally differentiated contribution of mountain glaciers and ice caps to future sea-level rise. Nature Geoscience, 4(2), 91--94.

[6]

Huss, M. (2011). Present and future cotribution of glacier storage change to runoff from macroscale drainage basins in europe. Water Resources Research, 47(7), W07511.

[7]

Wheate, R. (2010). Remote sensing of glaciers: techniques for topographic, spatial, and thematic mapping of glaciers.

[8]

Saraswat, P., Syed, T. H., Famiglietti, J. S., Fielding, E. J., Crippen, R., & Gupta, N. (2013). Recent changes in the snout position and surface velocity of gangotri glacier observed from space. International Journal of Remote Sensing, 34(24), 8653--8668.

Digital Library

[9]

Berger, M., Moreno, J., Johannessen, J. A., Levelt, P. F., & Hanssen, R. F. (2012). Esa\"s sentinel missions in support of earth system science. Remote Sensing of Environment, 120(none), 0--90.

[10]

Frank, P., Solveig, W., Andreas, K., Thomas, N., & Gabriele, S. (2016). Glacier remote sensing using sentinel-2. part ii: mapping glacier extents and surface facies, and comparison to landsat 8. Remote Sensing, 8(7), 575-.

[11]

Pope, A., & Rees, W. G. (2014). Impact of spatial, spectral, and radiometric properties of multispectral imagers on glacier surface classification. Remote Sensing of Environment, 141, 1--13.

[12]

Nascetti, A., Nocchi, F., Camplani, A., Rico, C. D., & Crespi, M. (2016). Exploiting sentinel-1 amplitude data for glacier surface velocity field measurements: feasibility demonstration on baltoro glacier. ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences.

[13]

Zbyněk Malenovsky, Rott, H., Cihlar, J., Schaepman, M. E., Glenda García-Santos, & Fernandes, R., et al. (2012). Sentinels for science: potential of sentinel-1, -2, and -3 missions for scientific observations of ocean, cryosphere, and land. Remote Sensing of Environment, 120(none), 0--101.

[14]

Griffith, J. (1979). Remote sensing and image interpretation. John Wiley & Sons.

[15]

Donghui, S., Shiyin, L., Yongjian, D., Liangfu, D., & Gang, L. I. (2004). Glacier changes at the head of yurungkax river in the west kunlun mountains in the past 32 years. Acta Geographica Sinica.

[16]

Rott, H., & Markl, G. (1989). Improved snow and glacier monitoring by the Landsat Thematic Mapper. In Proceedings of a Workshop on Landsat Thematic Mapper Applications (pp. 3--12).

[17]

Robson B A, Nuth C, Dahl S O, et al. (2015). Automated classification of debris-covered glaciers combining optical, sar and topographic data in an object-based environment. Remote Sensing of Environment, 170, 372--387.

[18]

Lang, S. (2008). Object-based image analysis for remote sensing applications: modeling reality - dealing with complexity. Object-Based Image Analysis. Springer Berlin Heidelberg.

[19]

Kraaijenbrink, P. D. A., Shea, J. M., Pellicciotti, F., Jong, S. M. D., & Immerzeel, W. W. (2016). Object-based analysis of unmanned aerial vehicle imagery to map and characterise surface features on a debris-covered glacier. Remote Sensing of Environment, 186, 581--595.

[20]

Lillesand, T., Kiefer, R. W., & Chipman, J. (2015). Remote sensing and image interpretation. John Wiley & Sons.

[21]

Schmidhuber, J. (2015). Deep learning in neural networks: An overview. Neural networks, 61, 85--117.

[22]

Bengio, Y., Courville, A., & Vincent, P. (2013). Representation learning: a review and new perspectives. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(8), 1798--1828.

Digital Library

[23]

Lecun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436.

[24]

Jie, H., Li, S., Albanie, S., Gang, S., & Wu, E. (2017). Squeeze-and-excitation networks., PP(99), 1--1.

Cited By

Huang CYou SLiu ALi PZhang JDeng J(2023)High-Resolution National-Scale Mapping of Paddy Rice Based on Sentinel-1/2 DataRemote Sensing10.3390/rs1516405515:16(4055)Online publication date: 16-Aug-2023
https://doi.org/10.3390/rs15164055
Thomas DRobson BRacoviteanu A(2023)An integrated deep learning and object-based image analysis approach for mapping debris-covered glaciersFrontiers in Remote Sensing10.3389/frsen.2023.11615304Online publication date: 10-Jul-2023
https://doi.org/10.3389/frsen.2023.1161530

Index Terms

Automatic Classification of Glaciers from Sentinel-2 Imagery Using A Novel Deep Learning Model
1. Applied computing
  1. Computers in other domains
    1. Cartography
2. Computing methodologies
  1. Machine learning

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cover image ACM Other conferences

ICAIP '19: Proceedings of the 2019 3rd International Conference on Advances in Image Processing

November 2019

232 pages

ISBN:9781450376754

DOI:10.1145/3373419

Copyright © 2019 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]

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Southwest Jiaotong University

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Publication History

Published: 24 January 2020

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ICAIP 2019

ICAIP 2019: 2019 3rd International Conference on Advances in Image Processing

November 8 - 10, 2019

Chengdu, China

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Cited By

Huang CYou SLiu ALi PZhang JDeng J(2023)High-Resolution National-Scale Mapping of Paddy Rice Based on Sentinel-1/2 DataRemote Sensing10.3390/rs1516405515:16(4055)Online publication date: 16-Aug-2023
https://doi.org/10.3390/rs15164055
Thomas DRobson BRacoviteanu A(2023)An integrated deep learning and object-based image analysis approach for mapping debris-covered glaciersFrontiers in Remote Sensing10.3389/frsen.2023.11615304Online publication date: 10-Jul-2023
https://doi.org/10.3389/frsen.2023.1161530

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