A Hybrid Metaheuristic and Deep Learning Approach for Change Detection in Remote Sensing Data

Authors

Volume: 12 | Issue: 5 | Pages: 9351-9356 | October 2022 | https://doi.org/10.48084/etasr.5246

Abstract

This study aimed to adapt Convolutional Neural Networks (CNN) to solve the problem of change detection using remote sensing imagery. Specifically, the goal was to investigate the impact of each CNN layer to detect changes between two satellite images acquired on two different dates. As low-level CNN layers detect fine details (small changes) and higher-level layers detect coarse details (large changes), the idea was to assign a weight to each layer and use a genetic algorithm based on a training dataset to generalize the detection process on the test dataset. The results showed the effectiveness of the proposed approach based on two real-life datasets.

Keywords:

Change detection, Remote sensing, Deep learning, Convolutional neural networks, Genetic Algorithms

Downloads

Download data is not yet available.

References

P. Coppin, E. Lambin, I. Jonckheere, and B. Muys, "Digital change detection methods in natural ecosystem monitoring: a review," in Analysis of Multi-Temporal Remote Sensing Images, vol. Volume 2, Singapore: World Scientific, 2002, pp. 3–36. DOI: https://doi.org/10.1142/9789812777249_0001

B. Demir, F. Bovolo, and L. Bruzzone, "Classification of Time Series of Multispectral Images With Limited Training Data," IEEE Transactions on Image Processing, vol. 22, no. 8, pp. 3219–3233, Dec. 2013. DOI: https://doi.org/10.1109/TIP.2013.2259838

J. Collomb, P. Balland, P. Francescato, Y. Gardet, D. Leh, and P. Saffré, "Thermomechanical Optimization and Comparison of a Low Thermal Inertia Mold with Rectangular Heating Channels and a Conventional Mold," Advances in Materials Science and Engineering, vol. 2019, May 2019, Art. no. e3261972. DOI: https://doi.org/10.1155/2019/3261972

T. L. Dammalage and N. T. Jayasinghe, "Land-Use Change and Its Impact on Urban Flooding: A Case Study on Colombo District Flood on May 2016," Engineering, Technology & Applied Science Research, vol. 9, no. 2, pp. 3887–3891, Apr. 2019. DOI: https://doi.org/10.48084/etasr.2578

S. Liu, L. Bruzzone, F. Bovolo, and P. Du, "Unsupervised Multitemporal Spectral Unmixing for Detecting Multiple Changes in Hyperspectral Images," IEEE Transactions on Geoscience and Remote Sensing, vol. 54, no. 5, pp. 2733–2748, Feb. 2016. DOI: https://doi.org/10.1109/TGRS.2015.2505183

X. Zheng, X. Chen, X. Lu, and B. Sun, "Unsupervised Change Detection by Cross-Resolution Difference Learning," IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1–16, 2022. DOI: https://doi.org/10.1109/TGRS.2021.3079907

A. M. E. Amin, Q. Liu, and Y. Wang, "Convolutional neural network features based change detection in satellite images," in First International Workshop on Pattern Recognition, Jul. 2016, vol. 10011, pp. 181–186.

G. Cao, B. Wang, H.-C. Xavier, D. Yang, and J. Southworth, "A new difference image creation method based on deep neural networks for change detection in remote-sensing images," International Journal of Remote Sensing, vol. 38, no. 23, pp. 7161–7175, Dec. 2017. DOI: https://doi.org/10.1080/01431161.2017.1371861

D. Peng and H. Guan, "Unsupervised change detection method based on saliency analysis and convolutional neural network," Journal of Applied Remote Sensing, vol. 13, no. 2, May 2019, Art. no. 024512. DOI: https://doi.org/10.1117/1.JRS.13.024512

R. Hedjam, A. Abdesselam, and F. Melgani, "Change Detection from Unlabeled Remote Sensing Images Using SIAMESE ANN," in IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan, Jul. 2019, pp. 1530–1533. DOI: https://doi.org/10.1109/IGARSS.2019.8898672

L. Khelifi and M. Mignotte, "Deep Learning for Change Detection in Remote Sensing Images: Comprehensive Review and Meta-Analysis," IEEE Access, vol. 8, pp. 126385–126400, 2020. DOI: https://doi.org/10.1109/ACCESS.2020.3008036

Y. Slimani and R. Hedjam, "Which Cnn Layer For Which Change? A Cnn Adaptation Approach For Change Detection In Remote Sensing Data," in 2020 Mediterranean and Middle-East Geoscience and Remote Sensing Symposium (M2GARSS), Tunis, Tunisia, Mar. 2020, pp. 5–8. DOI: https://doi.org/10.1109/M2GARSS47143.2020.9105168

M. D. Zeiler, G. W. Taylor, and R. Fergus, "Adaptive deconvolutional networks for mid and high level feature learning," in 2011 International Conference on Computer Vision, Aug. 2011, pp. 2018–2025. DOI: https://doi.org/10.1109/ICCV.2011.6126474

S. Sahel, M. Alsahafi, M. Alghamdi, and T. Alsubait, "Logo Detection Using Deep Learning with Pretrained CNN Models," Engineering, Technology & Applied Science Research, vol. 11, no. 1, pp. 6724–6729, Feb. 2021. DOI: https://doi.org/10.48084/etasr.3919

B. K. Alsaidi, B. J. Al-Khafaji, and S. A. A. Wahab, "Content Based Image Clustering Technique Using Statistical Features and Genetic Algorithm," Engineering, Technology & Applied Science Research, vol. 9, no. 2, pp. 3892–3895, Apr. 2019. DOI: https://doi.org/10.48084/etasr.2497

C. Benedek and T. Sziranyi, "Change Detection in Optical Aerial Images by a Multilayer Conditional Mixed Markov Model," IEEE Transactions on Geoscience and Remote Sensing, vol. 47, no. 10, pp. 3416–3430, Jul. 2009. DOI: https://doi.org/10.1109/TGRS.2009.2022633

B. L. Saux, R. C. Daudt, A. Boulch, and Y. Gousseau, "OSCD - Onera Satellite Change Detection." IEEE, Oct. 09, 2019, Accessed: Aug. 21, 2022. [Online]. Available: https://ieee-dataport.org/open-access/oscd-onera-satellite-change-detection.

A. A. Nielsen, "The Regularized Iteratively Reweighted MAD Method for Change Detection in Multi- and Hyperspectral Data," IEEE Transactions on Image Processing, vol. 16, no. 2, pp. 463–478, Oct. 2007. DOI: https://doi.org/10.1109/TIP.2006.888195

C. Wu, B. Du, and L. Zhang, "Slow Feature Analysis for Change Detection in Multispectral Imagery," IEEE Transactions on Geoscience and Remote Sensing, vol. 52, no. 5, pp. 2858–2874, Feb. 2014. DOI: https://doi.org/10.1109/TGRS.2013.2266673

T. Celik, "Unsupervised Change Detection in Satellite Images Using Principal Component Analysis and k-Means Clustering," IEEE Geoscience and Remote Sensing Letters, vol. 6, no. 4, pp. 772–776, Jul. 2009. DOI: https://doi.org/10.1109/LGRS.2009.2025059

W. A. Malila, "Change Vector Analysis: An Approach for Detecting Forest Changes with Landsat," in Symposium on Machine Processing of Remotely Sensed Data and Soid Information Systems and Remote Sensing and Soil Survey Proceedings, Jun. 1980, pp. 326–335.

K. L. de Jong and A. Sergeevna Bosman, "Unsupervised Change Detection in Satellite Images Using Convolutional Neural Networks," in 2019 International Joint Conference on Neural Networks (IJCNN), Budapest, Hungary, Jul. 2019, pp. 1–8. DOI: https://doi.org/10.1109/IJCNN.2019.8851762

Downloads

How to Cite

[1]
Y. Slimani and R. Hedjam, “A Hybrid Metaheuristic and Deep Learning Approach for Change Detection in Remote Sensing Data”, Eng. Technol. Appl. Sci. Res., vol. 12, no. 5, pp. 9351–9356, Oct. 2022.

Metrics

Abstract Views: 391
PDF Downloads: 209

Metrics Information
Bookmark and Share