Skip to main content
SpringerLink
Log in

Change detection - siamese based framework to detect changes over the earth’s surface (CD-CSNN)

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Recognizing the change over the earth’s surface in the context of remote sensing identities needs to find the differences between the images. The manual execution of this task will be erroneous and incompetent to scale. To automate this process, we propose a novel framework that uses a customized Siamese-based neural network pipeline (CSNN) to detect the changes over the earth’s surface. Based on the spatiotemporal analysis, the proposed work leverage’s the ancient divide and conquers strategy. The image is divided into sub-image, and the feature maps are extracted using convolution layers that detect the fine-grained changes which occur at the sub-image level. The proposed methodology uses the LEVIR-CD dataset, which has 637 images. The performance of the proposed change detection method increases, as the input image size gets reduced, this is experimentally proved by decreasing the original image size into sub-images of size 2 x 2. The best performance achieved at 2 x 2 sized sub-image is 94.15%, 87.0%, 92.0%, 89.40% for accuracy, precision, recall, and F1-score respectively, which outperforms the results of the baseline algorithm. Further, the proposed framework performs well on different terrains, with varying amounts and types of changes in the satellite image.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

Data Availability

Author declare that all the data being used in the design and production cum layout of the manuscript is declared in the manuscript.

References

  1. Abbas HK, Al-Saleh HK, Fatah NA, Mohammad HJ (2020) Statistical analysis of satellite images merge techniques based on edge detection. AIP Conference Proceedings :2290

  2. Aslam Muhammad Aqeel, et al. (2020) Noise removal from medical images using hybrid filters of technique. Journal of physics: Conference Series 4th, International Conference on Machine Vision and Information Technology (CMVIT 2020), pp 20–22

  3. Asokan A, Anitha J (2019) Change detection techniques for remote sensing applications: a survey. Earth Sci Inf 12:143–160. https://doi.org/10.1007/s12145-019-00380-5

    Article  Google Scholar 

  4. Bai T, Wang L, Yin D, Sun K, Chen Y, Li W, Li D (2022) Deep learning for change detection in remote sensing: a review, Geo-spatial Information Science. https://doi.org/10.1080/10095020.2022.2085633

  5. Benhur S (2020) A friendly introduction to Siamese networks. https://towardsdatascience.com/a-friendly-introduction-to-siamese-networks-85ab17522942

  6. Bontempi G, Taieb SB, Le-Borgne YA (2012) Machine learning strategies for time series forecasting. European business intelligence, Springer, pp 62–77

  7. Campbell JB, Wynne RH (2011) Introduction to remote sensing. Guilford Press

  8. Chen Jun, Dowman IJ, Songnian LI (2015) Information from imagery, ISPRS scientific vision and research agenda. ISPRS Journal of Photogrammetry and Remote Sensing. https://doi.org/10.1016/j.isprsjprs.2015.09.008

  9. Chen H, Shi Z (2020a) Levir-CD dataset. https://justchenhao.github.io/LEVIR/

  10. Chen H, Shi Z (2020b) A spatial-temporal attention-based method and a new dataset for remote sensing image change detection. Remote Sens 12(10)

  11. Deilami BR, Ahmad B, Saffar MRA, Umar HZ (2015) Review of change detection techniques from remotely sensed images. Remote sensing - Maxwell Scientific Publications

  12. Devi TG, Patil N (2020) Analysis & Evaluation of Image filtering Noise reduction technique for Microscopic Images. International Conference on Innovative Trends in Information Technology (ICITIIT), 1–6. https://doi.org/10.1109/ICITIIT49094.2020.9071556

  13. Dhruv B, Mittal N, Modi M (2017) Analysis of different filters for noise reduction in images. Recent Developments in Control Automation & Power Engineering (RDCAPE), 410–415. https://doi.org/10.1109/RDCAPE.2017.8358306

  14. Ghouaiel N, Lefevre S (2016) Coupling ground-level panoramas and aerial imagery for change detection. Geo-spatial Inf Sci 19(3):222–232

    Article  Google Scholar 

  15. Goel S (2020) Change detection using Siamese networks. https://towardsdatascience.com/change-detection-using-siamese-networks-fc2935-f82

  16. Gupta RK (2011) Change detection techniques for monitoring spatial urban growth of Jaipur city. Inst Town Planners India 8(3):88–104

    Google Scholar 

  17. Hendrycks D, Gimpel K (2016) Bridging nonlinearities and stochastic regularizes with Gaussian error linear units. OpenReview.net

  18. Hussain M, Chen D, Cheng A, Wei H, Stanley D (2013) Change detection from remotely sensed images: from pixel-based to object-based approaches. ISPRS J Photogramm Remote Sens 80:91–106

    Article  Google Scholar 

  19. Kasetkasem T, Varshney PK (2002) An image change detection algorithm based on Markov random field models. IEEE Trans Geosci Remote Sens 40(8):1815–1823. https://doi.org/10.1109/TGRS.2002.802498

    Article  Google Scholar 

  20. Liang Y, Veeravalli VV (2022) Non-Parametric quickest mean change detection. IEEE Transactions on Information Theory. https://doi.org/10.48550/arXiv.2108.11348

  21. Liu Y, Li Q, Yuan Y, Du Q, Wang D (2022) ABNEt: Adaptive balanced network for multiscale object detection in remote sensing imagery. In: IEEE transactions on geoscience and remote sensing vol. 60, pp 1–14. Art 5614914. https://doi.org/10.1109/TGRS.2021.3133956

  22. Lu D, Mausel P, Brondizio E, Moran E (2004) Change detection techniques. Int J Remote Sens 25(12):2365–2401

    Article  Google Scholar 

  23. Ma W, Xiong Y, Wu Y, Yang H, Zhang X, Jiao J (2019) Change detection in remote sensing images based on image mapping and a deep capsule network. Remote Sens 11(6):626–636

    Article  Google Scholar 

  24. Moustafa MS, Mohamed SA, Ahmed S, Nasr AH (2021) Hyperspectral change detection based on modification of UNet neural networks. J Appl Remote Sens 15(02). https://doi.org/10.1117/1.JRS.15.028505

  25. Mozgovoy DK, Hnatushenko V, Vasyliev VV (2018) Automated recognition of vegetation and water bodies on the territory of megacities in satellite images of visible and IR bands. ISPRS Ann Photogrammetr Remote Sens Spat Inf Sci 4 (3):167–172

    Article  Google Scholar 

  26. Nielsen A (2019) Practical time series analysis: prediction with statistics and machine learning. O’Reilly Media, Inc

  27. Oh KY, Jung HS, Lee KJ (2012) Comparison of image fusion methods to merge kompsat-2 panchromatic and multi-spectral images. Korean J Remote Sens 28(1):39–54

    Article  Google Scholar 

  28. Olofsson P, Bullock EL, Woodcock CE (2016) Time series analysis of satellite data reveals continuous deforestation of new England since the 1980’s. Environ Res Lett 11(6)

  29. Polykretis C, Grillakis MG, Alexakis DD (2020) Land cover change detection in Crete Island, Greece, using different combinations of biophysical indices in change vector analysis. EGU General Assembly 2020(4):8. https://doi.org/10.5194/egusphere-egu2020-4976

    Google Scholar 

  30. Public Health CUMS (2020) Spatio temporal analysis. https://www.publichealth.columbia.edu/research/population-health-methods/spatiotemporal-analysis

  31. Qin D, Zhou X, Zhou X, Huang G, Horan B, He J (2018) MSIM: A change detection framework for damage assessment in natural disasters. Exp Syst Appl 97:372–383

    Article  Google Scholar 

  32. Radke RJ, Andra S, Al-Kofahi O, Roysam B (2005) Image change detection algorithms: a systematic survey. IEEE Trans Image Process 14(3):294–307

    Article  MathSciNet  Google Scholar 

  33. Sharma V, Soni D, Srivastava D (2019) Filtration based noise reduction technique in an image. In: 4th International conference on internet of things: smart innovation and usages (IoT-SIU), pp 1–6. https://doi.org/10.1109/IoT-SIU.2019.8777623

  34. Shi W, Zhang M, Zhang R, Chen S, Zhan Z (2010) Change detection based on artificial intelligence: state-of-the-art and challenges. Remote Sens 12(10)

  35. Suribabu C, Bhaskar J, Neelakantan T (2012) Land use/cover change detection of Tiruchanapalli city, India, using integrated remote sensing and gis tools. J Indian Soc Remote Sens 40(4):699–708

    Article  Google Scholar 

  36. Tomowski D, Ehlers M, Klonus S (2011) Colour and texture based change detection for urban disaster analysis. IEEE international conference on joint urban remote sensing event, pp 329–332

  37. Turner MG (1990) Spatial and temporal analysis of landscape. Patterns Landscape Ecol 4(1):21–30

    Article  MathSciNet  Google Scholar 

  38. Wang M, Jia X, Wang Y, Chen Y (2020) A deep Siamese network with hybrid convolution feature extraction module for change detection based on multi-sensor remote sensing images. Remote Sensing, vol. 12(2). https://doi.org/10.3390/rs12020205. https://www.mdpi.com/2072-4292/12/2/205

  39. Wang Q, Liu Y, Xiong Z, Yuan Y (2022) Hybrid feature aligned network for salient object detection in optical remote sensing imagery. in. In: IEEE transactions on geoscience and remote sensing, vol. 60, pp 1–15. Art 5624915. https://doi.org/10.1109/TGRS.2022.3181062

  40. Wang Q, Yuan Z, Du Q, Li X (2019) GETNET: a general End-to-End 2-D CNN framework for hyperspectral image change detection. IEEE Trans Geosci Remote Sens 57(1):3–13. https://doi.org/10.1109/TGRS.2018.2849692

    Article  Google Scholar 

  41. Willhauck G (2000) Comparison of object oriented classification techniques and standard image analysis for the use of change detection between SPOT multispectral satellite images and aerial photos. Int Arch Photogrammetr Remote Sens 33:214–221

    Google Scholar 

  42. Xia Lei Song Min, Jin Junlan, Qian Ming, Zhang Yonghong (2021) SUACDNEt: attentional change detection network based on Siamese U-shaped structure. International Journal of Applied Earth Observation and Geo-information 105. https://doi.org/10.1016/j.jag.2021.102597

  43. Yin C, Xiong Z, Chen H, Wang J, Cooper D, David B (2015) A literature survey on smart cities. Sci China Inf Sci 58(10):1–18

    Article  Google Scholar 

  44. Zhou Shuting, Dong Zhen, Wang Guojie (2022) Machine-learning-based change detection of newly constructed areas from GF-2 imagery in nanjing, China. Remote Sens 14(2874). https://doi.org/10.3390/rs14122874

  45. Zhou X, Wang YC (2011) Spatial-temporal dynamics of urban green space in response to rapid urbanization and greening policies. Landscape Urban Planning 100(3):268–277

    Article  Google Scholar 

Download references

Funding

The author received no specific funding for this study.

Author information

Authors and Affiliations

  1. Department of Computer Science & Engineering, RV Institute of Technology & Management, Bengaluru, Karnataka, India

    Deepak N. A.

Authors
  1. Deepak N. A.
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Deepak N. A..

Ethics declarations

Conflict of Interests

The author declare that they have no conflicts of interest to report regarding the present study.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

A., D.N. Change detection - siamese based framework to detect changes over the earth’s surface (CD-CSNN). Multimed Tools Appl 83, 11387–11409 (2024). https://doi.org/10.1007/s11042-023-15562-z

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-023-15562-z

Keywords

Search

Navigation