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Towards Misregistration-Tolerant Change Detection using Deep Learning Techniques with Object-Based Image Analysis

Authors:

Dalton D. LungaAuthors Info & Claims

SIGSPATIAL '19: Proceedings of the 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems

Pages 420 - 423

https://doi.org/10.1145/3347146.3359068

Published: 05 November 2019 Publication History

Abstract

Co-registrating is a common pre-processing step for existing change detection algorithms, but registering bi-temporal images is nontrivial. The use of image patch as input for deep learning techniques provides a natural avenue to apply them in the OBIA framework, and have shown successful performance in the object-based land cover mapping and change detection applications. Even though attempts of applying deep learning techniques for change detection applications have been made with varying success, its application under OBIA framework for change detection have not been conducted and its tolerance for misregistration among temporal images are neither known. This study performed change detection under OBIA framework using deep learning techniques for the first time, and evaluated its performance regarding their tolerance of image misregistration on training and testing dataset. Our results demonstrate the proposed change detection scheme is surprisingly robust to image misregistration on the testing dataset, while classifiers trained with the training dataset containing image misregistration errors suffer from slight decrease of overall accuracy.

References

[1]

Radhakrishna Achanta, Appu Shaji, Kevin Smith, Aurelien Lucchi, Pascal Fua, and Sabine SÃijsstrunk. 2012. SLIC superpixels compared to state-of-the-art superpixel methods. IEEE transactions on pattern analysis and machine intelligence 34, 11 (2012), 2274--2282.

[2]

Gang Chen, Geoffrey J Hay, Luis MT Carvalho, and Michael A Wulder. 2012. Object-based change detection. International Journal of Remote Sensing 33, 14 (2012), 4434--4457.

[3]

Gang Chen, Kaiguang Zhao, and Ryan Powers. 2014. Assessment of the image misregistration effects on object-based change detection. ISPRS journal of photogrammetry and remote sensing 87 (2014), 19--27.

[4]

Alexis Comber, Peter Fisher, and Richard Wadsworth. 2004. Assessment of a semantic statistical approach to detecting land cover change using inconsistent data sets. Photogrammetric Engineering Remote Sensing 70, 8 (2004), 931--938.

[5]

Xiaolong Dai and Siamak Khorram. 1998. The effects of image misregistration on the accuracy of remotely sensed change detection. IEEE Transactions on Geoscience and Remote sensing 36, 5 (1998), 1566--1577.

[6]

Hernani Goncalves, LuÃηs Corte-Real, and Jose A Goncalves. 2011. Automatic image registration through image segmentation and SIFT. IEEE Transactions on Geoscience and Remote Sensing 49, 7 (2011), 2589--2600.

[7]

Maoguo Gong, Tao Zhan, Puzhao Zhang, and Qiguang Miao. 2017. Superpixel-based difference representation learning for change detection in multispectral remote sensing images. IEEE Transactions on Geoscience and Remote Sensing 55, 5 (2017), 2658--2673.

[8]

Maoguo Gong, Shengmeng Zhao, Licheng Jiao, Dayong Tian, and Shuang Wang. 2014. A novel coarse-to-fine scheme for automatic image registration based on SIFT and mutual information. IEEE Transactions on Geoscience and Remote Sensing 52, 7 (2014), 4328--4338.

[9]

A. Karpatne, Z. Jiang, R. R. Vatsavai, S. Shekhar, and V. Kumar. 2016. Monitoring Land-Cover Changes: A Machine-Learning Perspective T2 - IEEE Geoscience and Remote Sensing Magazine. IEEE Geoscience and Remote Sensing Magazine 4, 2 (2016), 8. https://doi.org/10.1109/MGRS.2016.2528038

[10]

Jacqueline Le Moigne, Nathan S Netanyahu, and Roger D Eastman. 2011. Image registration for remote sensing. Cambridge University Press.

[11]

Jia Liu, Maoguo Gong, Kai Qin, and Puzhao Zhang. 2016. A deep convolutional coupling network for change detection based on heterogeneous optical and radar images. IEEE transactions on neural networks and learning systems 29, 3 (2016), 545--559.

[12]

Tao Liu and Amr Abd-Elrahman. 2018. Deep convolutional neural network training enrichment using multi-view object-based analysis of Unmanned Aerial systems imagery for wetlands classification. ISPRS Journal of Photogrammetry and Remote Sensing 139 (2018), 154--170.

[13]

Tao Liu and Amr Abd-Elrahman. 2018. An object-based image analysis method for enhancing classification of land covers using fully convolutional networks and multi-view images of small unmanned aerial system. Remote Sensing 10, 3 (2018), 457.

[14]

Tao Liu, Amr Abd-Elrahman, Jon Morton, and Victor L Wilhelm. 2018. Comparing fully convolutional networks, random forest, support vector machine, and patch-based deep convolutional neural networks for object-based wetland mapping using images from small unmanned aircraft system. GIScience & remote sensing 55, 2 (2018), 243--264.

[15]

Tao Liu, Amr Abd-Elrahman, Alina Zare, Bon A Dewitt, Luke Flory, and Scot E Smith. 2018. A fully learnable context-driven object-based model for mapping land cover using multi-viewdata from unmanned aircraft systems. Remote sensing of environment 216 (2018), 328--344.

[16]

Wenping Ma, Zelian Wen, Yue Wu, Licheng Jiao, Maoguo Gong, Yafei Zheng, and Liang Liu. 2017. Remote sensing image registration with modified SIFT and enhanced feature matching. IEEE Geoscience and Remote Sensing Letters 14, 1 (2017), 3--7.

[17]

Lichao Mou, Lorenzo Bruzzone, and Xiao Xiang Zhu. 2019. Learning spectral-spatial-temporal features via a recurrent convolutional neural network for change detection in multispectral imagery. IEEE Transactions on Geoscience and Remote Sensing 57, 2 (2019), 924--935.

[18]

A. Tewkesbury. 2011. Mapping the extent of urban creep in Exeter using OBIA T2 - Proceedings of RSPSoc Annual Conference. (2011), 163.

[19]

John RG Townshend, Christopher O Justice, Charlotte Gurney, and James McManus. 1992. The impact of misregistration on change detection. IEEE Transactions on Geoscience and remote sensing 30, 5 (1992), 1054--1060.

[20]

Andrea Vedaldi and Stefano Soatto. [n.d.]. Quick shift and kernel methods for mode seeking. In European Conference on Computer Vision. Springer, 705--718.

[21]

Shuang Wang, Dou Quan, Xuefeng Liang, Mengdan Ning, Yanhe Guo, and Licheng Jiao. 2018. A deep learning framework for remote sensing image registration. ISPRS Journal of Photogrammetry and Remote Sensing 145 (2018), 148--164.

[22]

Yang Zhan, Kun Fu, Menglong Yan, Xian Sun, Hongqi Wang, and Xiaosong Qiu. 2017. Change detection based on deep siamese convolutional network for optical aerial images. IEEE Geoscience and Remote Sensing Letters 14, 10 (2017), 1845--1849.

[23]

Barbara Zitova and Jan Flusser. 2003. Image registration methods: a survey. Image and vision computing 21, 11 (2003), 977--1000.

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https://doi.org/10.1080/01431161.2024.2429783
Alcaras ECostantino DGuastaferro FParente CPepe M(2022)Normalized Burn Ratio Plus (NBR+): A New Index for Sentinel-2 ImageryRemote Sensing10.3390/rs1407172714:7(1727)Online publication date: 3-Apr-2022
https://doi.org/10.3390/rs14071727
Qin RLiu T(2022)A Review of Landcover Classification with Very-High Resolution Remotely Sensed Optical Images—Analysis Unit, Model Scalability and TransferabilityRemote Sensing10.3390/rs1403064614:3(646)Online publication date: 29-Jan-2022
https://doi.org/10.3390/rs14030646
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Index Terms

Towards Misregistration-Tolerant Change Detection using Deep Learning Techniques with Object-Based Image Analysis
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Supervised learning
        Supervised learning by classification
2. Information systems
  1. Information systems applications

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cover image ACM Conferences

SIGSPATIAL '19: Proceedings of the 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems

November 2019

648 pages

ISBN:9781450369091

DOI:10.1145/3347146

Copyright © 2019 Owner/Author.

Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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SIGSPATIAL: ACM Special Interest Group on Spatial Information

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Published: 05 November 2019

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SIGSPATIAL '19: 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems

November 5 - 8, 2019

IL, Chicago, USA

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SIGSPATIAL '19 Paper Acceptance Rate 34 of 161 submissions, 21%;

Overall Acceptance Rate 257 of 1,238 submissions, 21%

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Cited By

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https://doi.org/10.1080/01431161.2024.2429783
Alcaras ECostantino DGuastaferro FParente CPepe M(2022)Normalized Burn Ratio Plus (NBR+): A New Index for Sentinel-2 ImageryRemote Sensing10.3390/rs1407172714:7(1727)Online publication date: 3-Apr-2022
https://doi.org/10.3390/rs14071727
Qin RLiu T(2022)A Review of Landcover Classification with Very-High Resolution Remotely Sensed Optical Images—Analysis Unit, Model Scalability and TransferabilityRemote Sensing10.3390/rs1403064614:3(646)Online publication date: 29-Jan-2022
https://doi.org/10.3390/rs14030646
Peng BHuang QVongkusolkit JGao SWright DFang ZQiang Y(2021)Urban Flood Mapping With Bitemporal Multispectral Imagery Via a Self-Supervised Learning FrameworkIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing10.1109/JSTARS.2020.304767714(2001-2016)Online publication date: 2021
https://doi.org/10.1109/JSTARS.2020.3047677
Liu TYang L(2020)A Fully Automatic Method for Rapidly Mapping Impacted Area by Natural DisasterIGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium10.1109/IGARSS39084.2020.9323634(6906-6909)Online publication date: 26-Sep-2020
https://doi.org/10.1109/IGARSS39084.2020.9323634
Ghorbanzadeh OTiede DWendt LSudmanns MLang S(2020)Transferable instance segmentation of dwellings in a refugee camp - integrating CNN and OBIAEuropean Journal of Remote Sensing10.1080/22797254.2020.175945654:sup1(127-140)Online publication date: 6-May-2020
https://doi.org/10.1080/22797254.2020.1759456

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