Java and Bali Shoreline Change Detection Based on Structural Similarity Index Measurement of Multispectral Images

Authors

  • I Gede Wahyu Surya Dharma Universitas Bali Internasional
  • I Gede Karang Komala Putra Universitas Bali Internasional

DOI:

https://doi.org/10.38043/tiers.v4i2.4468

Keywords:

SSIM, HMRF, Shoreline, Multispectral Image

Abstract

The abstract effectively delineates the pertinent issues addressed in the research, presenting a clear exposition of the challenges associated with coastline monitoring in Indonesia. The methodology is well-defined, incorporating the utilization of Landsat images, Structural Similarity Index Measurement (SSIM), and the application of Hidden Markov Random Field for segmentation. Moreover, the influence of Indonesia's equatorial positioning on cloud cover and the subsequent application of morphological operations are appropriately highlighted. However, it is crucial to provide explicit details regarding the research findings. Specifically, the abstract should elucidate the specific outcomes or results obtained from the conducted experiments or analyses. This addition would enhance the clarity and scientific robustness of the abstract, ensuring that it accurately reflects the research objectives and their corresponding achievements. Inclusion of quantitative data or statistical analyses would be particularly valuable in this regard. This would not only bolster the abstract but also furnish a more comprehensive overview of the study's empirical contributions.

Downloads

Download data is not yet available.

References

M. A. Marfai, H. Almohammad, S. Dey, B. Susanto, and L. King, “Coastal dynamic and shoreline mapping: multi-sources spatial data analysis in Semarang Indonesia,” Environ. Monit. Assess., vol. 142, no. 1–3, pp. 297–308, Jul. 2008, doi: 10.1007/s10661-007-9929-2.

Y. Wang, X. Hou, P. Shi, and L. Yu, “Detecting Shoreline Changes in Typical Coastal Wetlands of Bohai Rim in North China,” Wetlands, vol. 33, no. 4, pp. 617–629, Aug. 2013, doi: 10.1007/s13157-013-0418-9.

A. Yavariabdi and H. Kusetogullari, “Change Detection in Multispectral Landsat Images Using Multiobjective Evolutionary Algorithm,” IEEE Geosci. Remote Sens. Lett., vol. 14, no. 3, pp. 414–418, Mar. 2017, doi: 10.1109/LGRS.2016.2645742.

H. Othman and S. E. Qian, “Noise reduction of hyperspectral imagery using hybrid spatial-spectral derivative-domain wavelet shrinkage,” IEEE Trans. Geosci. Remote Sens., vol. 44, no. 2, pp. 397–408, 2006, doi: 10.1109/TGRS.2005.860982.

D. S. Candra, S. Phinn, and P. Scarth, “Cloud and cloud shadow removal of landsat 8 images using Multitemporal Cloud Removal method,” in 2017 6th International Conference on Agro-Geoinformatics, IEEE, Aug. 2017, pp. 1–5. doi: 10.1109/Agro-Geoinformatics.2017.8047007.

R. Cao, Y. Chen, J. Chen, X. Zhu, and M. Shen, “Thick cloud removal in Landsat images based on autoregression of Landsat time-series data,” Remote Sens. Environ., vol. 249, p. 112001, Nov. 2020, doi: 10.1016/j.rse.2020.112001.

Y. Chen, Q. Weng, L. Tang, X. Zhang, M. Bilal, and Q. Li, “Thick Clouds Removing From Multitemporal Landsat Images Using Spatiotemporal Neural Networks,” IEEE Trans. Geosci. Remote Sens., vol. 60, pp. 1–14, 2022, doi: 10.1109/TGRS.2020.3043980.

S. Bostan, M. A. Ortak, C. Tuna, A. Akoguz, E. Sertel, and B. Berk Ustundag, “Comparison of classification accuracy of co-located hyperspectral & multispectral images for agricultural purposes,” 2016 5th International Conference on Agro-Geoinformatics, Agro-Geoinformatics 2016. 2016. doi: 10.1109/Agro-Geoinformatics.2016.7577671.

F. Wu, X. Y. Jing, X. You, D. Yue, R. Hu, and J. Y. Yang, “Multi-view low-rank dictionary learning for image classification,” Pattern Recognit., vol. 50, pp. 143–154, 2016, doi: 10.1016/j.patcog.2015.08.012.

S. Li, T. Lu, L. Fang, X. Jia, and J. A. Benediktsson, “Probabilistic Fusion of Pixel-Level and Superpixel-Level Hyperspectral Image Classification,” IEEE Trans. Geosci. Remote Sens., vol. 54, no. 12, pp. 7416–7420, 2016, doi: 10.1109/TGRS.2016.2603190.

C. M. Bishop, Pattern Recognition and Machine Learning (Information Science and Statistics). Berlin, Heidelberg: Springer-Verlag, 2006.

F. Wang, R. Zhang, and Q. Wu, “Hyperspectral image classification based on PCA network,” Work. Hyperspectral Image Signal Process. Evol. Remote Sens., vol. 0, pp. 1–5, 2016, doi: 10.1109/WHISPERS.2016.8071787.

W. Burger and M. J. Burge, Digital Image Processing. in Texts in Computer Science. London: Springer London, 2016. doi: 10.1007/978-1-4471-6684-9.

H. Liu, J. Sun, H. Sun, and H. Lin, “Image inpainting based on hidden Markov random field,” in 2016 IEEE 13th International Conference on Signal Processing (ICSP), IEEE, Nov. 2016, pp. 697–701. doi: 10.1109/ICSP.2016.7877922.

A. Blake, P. Kohli, and C. Rother, Markov Random Fields for Vision and Image Processing. The MIT Press, 2011. doi: 10.7551/mitpress/8579.001.0001.

S. Kumar and R. L. Velusamy, “Kernel approach for similarity measure in latent fingerprint recognition,” International Conference on Emerging Trends in Electrical, Electronics and Sustainable Energy Systems, ICETEESES 2016. pp. 368–373, 2016. doi: 10.1109/ICETEESES.2016.7581411.

A. Yavariabdi and H. Kusetogullari, “Change Detection in Multispectral Landsat Images Using Multiobjective Evolutionary Algorithm,” IEEE Geosci. Remote Sens. Lett., vol. 14, no. 3, pp. 414–418, 2017, doi: 10.1109/LGRS.2016.2645742.

I. Bakurov, M. Buzzelli, R. Schettini, M. Castelli, and L. Vanneschi, “Structural similarity index (SSIM) revisited: A data-driven approach,” Expert Syst. Appl., vol. 189, p. 116087, Mar. 2022, doi: 10.1016/j.eswa.2021.116087.

X. Zhong et al., “Fuzzy Nonlinear Proximal Support Vector Machine for Land Extraction Based on Remote Sensing Image,” PLoS One, vol. 8, no. 7, p. e69434, Jul. 2013, doi: 10.1371/journal.pone.0069434.

J. Huang, Y. Ma, X. Mei, and F. Fan, “A hybrid spatial-spectral denoising method for infrared hyperspectral images using 2DPCA,” Infrared Phys. Technol., vol. 79, pp. 68–73, Nov. 2016, doi: 10.1016/j.infrared.2016.09.009.

C. A. Shah, “Automated lake shoreline mapping at subpixel accuracy,” IEEE Geosci. Remote Sens. Lett., vol. 8, no. 6, pp. 1125–1129, 2011, doi: 10.1109/LGRS.2011.2157951.

Downloads

Published

2023-12-25

How to Cite

1.
Dharma IGWS, Putra IGKK. Java and Bali Shoreline Change Detection Based on Structural Similarity Index Measurement of Multispectral Images. TIERS [Internet]. 2023Dec.25 [cited 2024Jul.12];4(2):92-103. Available from: https://journal.undiknas.ac.id/index.php/tiers/article/view/4468

Issue

Section

Articles