Abstract
Change Detection(CD), in the context of remote sensing, determines the differences in different portions of land images when studied over a while. Human change detection and analysis are limited and prone to errors and are incompetent for the scale and speed required for processing satellite data. Automating CD of earth surface features helps humans develop a deeper understanding of the changes in natural phenomena. In this work, we propose a novel framework for detecting the changes in the satellite images using the Siamese based neural network pipeline. Based on Spatio-Temporal analysis, our approach leverages the divide and conquer paradigm to divide the original image into sub-images and then uses the convolution layers to extract the feature maps at a sub-image level to detect fine-grained changes. To evaluate our proposed approach, we used a recently released dataset in 2020, LEVIR-CD, which consists of 637 pairs of high resolution images. In our work, we experimentally establish that decreasing the sub-image size of the original input image increases the accuracy of change detection, with the best performance achieved at 2 × 2 sub-image level with the best recall and F1-score with values 92% and 91.5%, respectively, outperforming the previous best results. Further, we find that our novel framework performs well on different terrains, with varying amounts and types of changes in the satellite image.
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Abbas HK, Al-Saleh AH, Fatah NA, Mohamad HJ (2020) Statistical analysis of sattelite images merge techniques based on edge detection. In: AIP Conference Proceedings, AIP Publishing LLC, vol 2290, p 050017
Asokan A, Anitha J (2019) Change detection techniques for remote sensing applications: a survey. Earth Sci Inf 12(2):143–160
Benhur S (2020) A friendly introduction to siamese networks. https://towardsdatascience.com/a-friendly-introduction-to-siamese-networks-85ab17522942. Accessed 26 April 2023
Bontempi G, Taieb SB, Le Borgne YA (2012) Machine learning strategies for time series forecasting. In: European business intelligence summer school, Springer, pp 62–77
Bruzzone L, Prieto D F (2000) Automatic analysis of the difference image for unsupervised change detection. IEEE Trans Geosci Remote Sens 38 (3):1171–1182
Campbell JB, Wynne RH (2011) Introduction to remote sensing. Guilford Press
Chen G, Hay G J, Carvalho L M, Wulder M A (2012) Object-based change detection. Int J Remote Sens 33(14):4434–4457
Chen H, Shi Z (2020a) LEVIR-CD dataset. https://justchenhao.github.io/LEVIR/. Accessed 26 April 2023
Chen H, Shi Z (2020b) A spatial-temporal attention-based method and a new dataset for remote sensing image change detection, vol 12. https://doi.org/10.3390/rs12101662. https://www.mdpi.com/2072-4292/12/10/1662. Accessed 26 April 2023
Columbia University Mailman School of Public Health (2020) Spatiotemporal analysis. https://www.publichealth.columbia.edu/research/population-health-methods/spatiotemporal-analysis. Accessed 23 April 2023
Daudt R C, Le Saux B, Boulch A (2018) Fully convolutional siamese networks for change detection. In: 2018 25th IEEE international conference on image processing (ICIP), IEEE, pp 4063–4067
Daudt RC, Le Saux B, Boulch A, Gousseau Y (2018a) Urban change detection for multispectral earth observation using convolutional neural networks. In: IGARSS 2018-2018 IEEE international geoscience and remote sensing symposium, IEEE, pp 2115–2118
Ghouaiel N, Lefèvre S (2016) Coupling ground-level panoramas and aerial imagery for change detection. Geo-spatial Inf Sci 19(3):222–232
Goel S (2020) Change detection using siamese networks. https://towardsdatascience.com/change-detection-using-siamese-networks-fc2935ffff82. Accessed 26 April 2023
Gupta R K (2011) Change detection techniques for monitoring spatial urban growth of Jaipur city. Inst Town Planners India J 8(3):88–104
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. https://doi.org/10.1016/j.isprsjprs.2013.03.006, https://www.sciencedirect.com/science/article/pii/S0924271613000804
Im J, Jensen J, Tullis J (2008) Object-based change detection using correlation image analysis and image segmentation. Int J Remote Sens 29(2):399–423
Jaderberg M, Simonyan K, Zisserman A, et al. (2015) Spatial transformer networks
Kasetkasem T, Varshney P K (2002) An image change detection algorithm based on Markov random field models. IEEE Trans Geosci Remote Sens 40(8):1815–1823
Ke Q, Zhang P (2021) CS-HSNet: a cross-siamese change detection network based on hierarchical-split attention. IEEE J Sel Top Appl Earth Obs Remote Sens 14:9987–10002
Lu D, Mausel P, Brondizio E, Moran E (2004) Change detection techniques. Int J Remote Sens 25(12):2365–2401
Ma W, Xiong Y, Wu Y, Yang H, Zhang X, Jiao L (2019) Change detection in remote sensing images based on image mapping and a deep capsule network, vol 11. https://www.mdpi.com/2072-4292/11/6/626. Accessed 26 April 2023
Mahmoud A S, Mohamed S A, Moustafa M S, El-Khorib R A, Abdelsalam H M, El-Khodary I A (2021) Training compact change detection network for remote sensing imagery. IEEE Access 9:90366–90378
Mozgovoy Dk, Hnatushenko VV, Vasyliev VV (2018) Automated recognition of vegetation and water bodies on the territory of megacities in satellite images of visible and IR bands. ISPRS Annals of Photogrammetry, Remote Sens Spat Inf Sci IV3:167–172. https://doi.org/10.5194/isprs-annals-IV-3-167-2018
Nielsen A (2019) Practical time series analysis: prediction with statistics and machine learning, O’Reilly Media Inc.
Oh K Y, Jung H S, Lee K J (2012) Comparison of image fusion methods to merge KOMPSAT-2 panchromatic and multispectral images. Korean Can J Remote Sens 28(1):39–54
Olofsson P, Holden C E, Bullock E L, Woodcock C E (2016) Time series analysis of satellite data reveals continuous deforestation of new england since the 1980s. Environ Res Lett 11(6):064002
Polykretis C, Grillakis M G, Alexakis D D (2020) Exploring the impact of various spectral indices on land cover change detection using change vector analysis: A case study of Crete Island, Greece. Remote Sens 12(2):319
Qin D, Zhou X, Zhou W, Huang G, Ren Y, Horan B, He J, Kito N (2018) MSIM: a change detection framework for damage assessment in natural disasters. Expert Syst Appl 97:372–383
Radke R J, Andra S, Al-Kofahi O, Roysam B (2005) Image change detection algorithms: a systematic survey. IEEE Trans Image Process 14(3):294–307
Rokni K, Ahmad A, Solaimani K, Hazini S (2015) A new approach for surface water change detection: Integration of pixel level image fusion and image classification techniques. Int J Appl Earth Obs Geoinf 34:226–234
Shafique A, Cao G, Khan Z, Asad M, Aslam M (2022) Deep learning-based change detection in remote sensing images: a review. Remote Sens 14 (4):871
Shi W, Zhang M, Zhang R, Chen S, Zhan Z (2020) Change detection based on artificial intelligence: State-of-the-art and challenges, vol 12. https://doi.org/10.3390/rs12101688. https://www.mdpi.com/2072-4292/12/10/1688. Accessed 26 April 2023
Song K, Jiang J (2021) Agcdetnet: an attention-guided network for building change detection in high-resolution remote sensing images. IEEE J Sel Top Appl Earth Obs Remote Sens 14:4816–4831
Suribabu C, Bhaskar J, Neelakantan T (2012) Land use/cover change detection of Tiruchirapalli City, India, using integrated remote sensing and GIS tools. J Indian Soc Remote Sens 40(4):699–708
Tewkesbury A P, Comber A J, Tate N J, Lamb A, Fisher P F (2015) A critical synthesis of remotely sensed optical image change detection techniques. Remote Sens Environ 160:1–14
Tomowski D, Ehlers M, Klonus S (2011) Colour and texture based change detection for urban disaster analysis. In: 2011 Joint Urban Remote Sensing Event, IEEE, pp 329–332
Turner M G (1990) Spatial and temporal analysis of landscape patterns. Landsc Ecol 4(1):21–30
Venugopal N (2020) Automatic semantic segmentation with deeplab dilated learning network for change detection in remote sensing images. Neural Process Lett 51(3):2355–2377
Wang M, Tan K, Jia X, Wang X, Chen Y (2020) A deep siamese network with hybrid convolutional feature extraction module for change detection based on multi-sensor remote sensing images, vol 12. https://doi.org/10.3390/rs12020205. https://www.mdpi.com/2072-4292/12/2/205. Accessed 26 April 2023
Wang Q, Yuan Z, Du Q, Li X (2018) GETNET: A general end-to-end 2-D CNN framework for hyperspectral image change detection. IEEE Trans Geosci Remote Sens 57(1):3–13
Willhauck G, Schneider T, De Kok R, Ammer U (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. In: Proceedings of XIX ISPRS congress, Amsterdam: IAPRS, vol 33. pp 35–42
Yin C, Xiong Z, Chen H, Wang J, Cooper D, David B (2015) A literature survey on smart cities. Science China Inf Sci 58(10):1–18
Zhang Y, Deng M, He F, Guo Y, Sun G, Chen J (2021) FODA: Building change detection in high-resolution remote sensing images based on feature–output space dual-alignment. IEEE J Sel Top Appl Earth Obs Remote Sens 14:8125–8134
Zheng Y, Zhang X, Hou B, Liu G (2013) Using combined difference image and k-means clustering for sar image change detection. IEEE Geosci Remote Sens Lett 11(3):691–695
Zhou X, Wang Y C (2011) Spatial–temporal dynamics of urban green space in response to rapid urbanization and greening policies. Landsc Urban Plan 100(3):268–277
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Riya Agarwal, Shaifali Jindal and Shradha Narain are contributed equally to this work.
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Agarwal, R., Jindal, S., Narain, S. et al. A novel framework for fine-grained spatio-temporal change detection in satellite images. Multimed Tools Appl 83, 1241–1260 (2024). https://doi.org/10.1007/s11042-023-14705-6
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DOI: https://doi.org/10.1007/s11042-023-14705-6