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OBCD-HH: an object-based change detection approach using multi-feature non-seed-based region growing segmentation

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Abstract

There is an increasing need to get updated information regarding the changes on earth’s surface. The information obtained can be used in a wide range of applications including disaster management, land-use investigation etc. The high-resolution remote sensing images obtained from satellites provide us with an opportunity to detect changes on earth’s surface between various time intervals. In this paper, an unsupervised object-based change detection (OBCD) method is proposed to detect changes in high resolution bi-temporal satellite images. To detect changes, a novel multi-feature non-seed-based region growing (MF-NSRG) algorithm is proposed for image segmentation based on heterogeneity minimization that uses textural heterogeneity along with spectral and spatial heterogeneity during region growing. The performance of MF-NSRG algorithm is further improved by using Harris Hawk, a recently proposed metaheuristic algorithm, which is used to obtain optimal values of segmentation parameters. Finally, the feature maps extracted from the pre-change and post-change segmented images are analysed using histogram trend similarity (HTS) approach to detect changes. The proposed approach is known as object-based change detection using Harris Hawk (OBCD-HH). The proposed OBCD-HH approach is applied on two datasets: xBD and Onera Satellite Change Detection (OSCD) dataset. Its performance is compared with existing state-of-the-art algorithms and results show the superiority of the proposed approach.

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References

  1. Adams R, Bischof L (1994) Seeded region growing. IEEE Trans PAMI 16(6):641–647

    Article  Google Scholar 

  2. Aldhshan SRS, Shafri HZ (2019) Change detection on land use/land cover and land surface temperature using spatiotemporal data of Landsat: a case study of Gaza Strip. Arab J Geosci 12:443. https://doi.org/10.1007/s12517-019-4597-4

    Article  Google Scholar 

  3. Amit SNKB, Shiraishi S, Inoshita T, Aoki Y (2016) Analysis of satellite images for disaster detection. In: IEEE international Geoscience and remote sensing symposium (IGARSS), IEEE 5189–5192

  4. Baatz M, Schape A (2000) Multiresolution Segmentation: An Optimization Approach for High Quality Multi-Scale Image Segmentation. In: Strobl J, Blaschke T, Griesbner G (eds) Angewandte Geographische Informations-Verarbeitung, XII. Wichmann Verlag, Karlsruhe, pp 12–23

    Google Scholar 

  5. Bie CAJM, Khan MR, Toxopeus AG, Venus V, Skidmore A (2008) Hypertemporal image analysis for crop mapping and change detection. In: ISPRS 2008. Proceedings of the XXI congress: Silk road for information from imagery: the International Society for Photogrammetry and Remote Sensing, 803–812

  6. Bolorinos J, Ajami NK, Rajagopal R (2020) Consumption Change Detection for Urban Planning: Monitoring and Segmenting Water Customers During Drought. Water Resour Res 56(3):1–16

    Article  Google Scholar 

  7. Bruzzone L, Prieto DF (2000) Automatic analysis of the difference image for unsupervised change detection. IEEE Trans Geosci Remote Sens 38(3):1171–1182

    Article  Google Scholar 

  8. Cao G, Liu Y, Shang Y (2014) Automatic change detection in remote sensing images using level set method with neighborhood constraints. J Appl Remote Sens 8(1). https://doi.org/10.1117/1.JRS.8.083678

  9. Celik T (2009) Unsupervised change detection in satellite images using principal component analysis and k-means clustering. IEEE Geosci Remote Sens Lett 6(4):772–776

    Article  MathSciNet  Google Scholar 

  10. Celik T (2010) Change detection in satellite images using a genetic algorithm approach. IEEE Geosci Remote Sens Lett 7(2):386–390

    Article  Google Scholar 

  11. Chen H, Shi Z (2020) A spatial-temporal attention-based method and a new dataset for remote sensing image change detection. Remote Sens 12(10):1662

    Article  Google Scholar 

  12. Chen J, Gong P, He C, Pu R, Shi P (2003) Land-use/land-cover change detection using improved change-vector analysis. Photogramm Eng Remote Sens 69:369–379

    Article  Google Scholar 

  13. Chen S, Yang K, Stiefelhagen R (2021) DR-TANet: dynamic receptive temporal attention network for street scene change detection. arXiv:2103.00879

  14. Daudt RC, Saux BL, Boulch A (2018) Fully Convolutional Siamese Networks for Change Detection. In: Proceedings of the 25th IEEE International Conference on Image Processing (ICIP) 4063–4067

  15. Daudt RC, Le Saux B, Boulch A, Gousseau Y (2018) Urban Change Detection for Multispectral Earth Observation Using Convolutional Neural Networks. In IEEE International Geoscience and Remote Sensing Symposium (IGARSS) 2115–2118

  16. De Alwis Pitts DA, So E (2017) Enhanced Change Detection Index for Disaster Response, Recovery Assessment and Monitoring of Accessibility and Open Spaces (Camp Sites). Int J Appl Earth Obs Geoinf 57:49–60. https://doi.org/10.1016/j.jag.2016.12.004

    Article  Google Scholar 

  17. Do CB, Batzoglou S (2008) What is the expectation maximization algorithm? Nat Biotechnol 26(8):897–899

    Article  Google Scholar 

  18. Fan J, Yau DK, Elmagarmid AK, Aref WG (2001) Automatic image segmentation by integrating color-edge extraction and seeded region growing. IEEE Trans Image Process 10(10):1454–1466

    Article  Google Scholar 

  19. Fujita A, Sakurada K, Imaizumi T, Ito R, Hikosaka S, Nakamura R (2017) Damage detection from aerial images via convolutional neural networks. In: Fifteenth IAPR International Conference on Machine Vision Applications, IEEE Press 5–8

  20. Gupta R, Hosfelt R, Sajeev S, Patel N, Goodman B, Doshi J, Heim E, Choset H, Gaston M (2019) xBD: A dataset for assessing building damage from satellite imagery. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops 10–17

  21. Han Y, Javed A, Jung S, Liu S (2020) Object-based change detection of very high resolution images by fusing pixel-based change detection results using weighted dempste-shafer theory. Remote Sens 12(6):983. https://doi.org/10.3390/rs12060983

    Article  Google Scholar 

  22. Haralick RM, Shanmugam K, Dinstein IH (1993) Textural features for image classification. IEEE Trans Syst Man Cybern SMC 3(6):610–621

    Article  Google Scholar 

  23. Hegazy I, Kaloop MR (2015) Monitoring urban growth and land use change detection with GIS and remote sensing techniques in Daqahlia Governorate Egypt. Int J Sustain Built Environ 4(1):117–124

    Article  Google Scholar 

  24. Heidari AA, Mirjalili S, Faris H, Aljarah I, Mafarja M, Chen H (2019) Harris hawks optimization: Algorithm and applications. Futur Gener Comput Syst 97:849–872

    Article  Google Scholar 

  25. Horowitz SL, Pavlidis T (1974) Picture segmentation by a directed split-and-merge procedure. Proc 2nd Int Joint Conf Pattern Recognit 424–433

  26. Hossain MD, Chen D (2019) Segmentation for Object-Based Image Analysis (OBIA): A review of algorithms and challenges from remote sensing perspective. ISPRS J Photogramm Remote Sens 150:115–134. https://doi.org/10.1016/j.isprsjprs.2019.02.009

    Article  Google Scholar 

  27. Ikonomakis N, Plataniotis KN, Zervakis M, Venetsanopoulos AN (1997) Region growing and region merging image segmentation. Int Conf Digit Signal Process DSP 1:299–301

    Article  Google Scholar 

  28. Ji S et al (2019) Building instance change detection from large-scale aerial images using convolutional neural networks and simulated samples. Remote Sens 11(11):1343

    Article  Google Scholar 

  29. Jian P, Chen K, Zhang C (2016) A Hypergraph-Based Context-Sensitive Representation Technique for VHR Remote-Sensing Image Change Detection. Int J Remote Sens 37(8):1814–1825. https://doi.org/10.1080/2150704X.2016.1163744

    Article  Google Scholar 

  30. Johnson BA, Bragais M, Endo I, Magcale-Macandog D, Macandog P (2015) Image Segmentation Parameter Optimization considering Within- and Between-Segment Heterogeneity at Multiple Scale Levels: Test Case for Mapping Residential Areas Using Landsat Imagery. ISPRS Int J Geo-Inf 4(4):2292–2305. https://doi.org/10.3390/ijgi4042292

    Article  Google Scholar 

  31. Jong KLD, Bosman AS (2019) Unsupervised Change Detection in Satellite Images Using Convolutional Neural Networks. Int Joint Conf Neural Netw (IJCNN):1–8. https://doi.org/10.1109/IJCNN.2019.8851762

  32. Kalpana V (2018) Analysis of rain fall and the temperature of Coimbatore District using land use and land cover change detection by image segmentation. Multimed Tools Appl 77:30487–30504. https://doi.org/10.1007/s11042-018-6125-z

    Article  Google Scholar 

  33. Karaboga D (2005) An idea based on honey bee swarm for numerical optimization. Technical report, Computer Engineering Department, Engineering Faculty, Erciyes University

  34. Kennedy J, Eberhart RC (1995) Particle swarm optimization. In: Proc. IEEE Conf. on Neural Networks, IV, Piscataway, NJ 1942–1948

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

    Article  Google Scholar 

  36. Koshimura S, Kayaba S, Gokon H (2011) Object-based Image Analysis of Post-tsunami High-resolution Satellite Images for Mapping the Impact of Tsunami Disaster. IEEE International Geoscience and Remote Sensing Symposium

  37. Liu S, Bruzzone L, Bovolo F, Zanetti M, Du P (2015) Sequential spectral change vector analysis for iteratively discovering and detecting multiple changes in hyperspectral images. IEEE Trans Geosci Remote Sens 53(8):4363–4378

    Article  Google Scholar 

  38. Liu R, Jiang D, Zhang L, Zhang Z (2020) Deep depthwise separable convolutional network for change detection in optical aerial images. IEEE J Sel Top Appl Earth Obs Remote Sens 13:1109–1118. https://doi.org/10.1109/JSTARS.2020.2974276

    Article  Google Scholar 

  39. Liu T, Yang L, Lunga D (2021) Change detection using deep learning approach with object-based image analysis. Remote Sens Environ 256:112308. https://doi.org/10.1016/j.rse.2021.112308

    Article  Google Scholar 

  40. Long X-Y (2008) A Method of Urban Change Detection Based on Image Segmentation: A Method of Urban Change Detection Based on Image Segmentation. Geo-Inf Sci 10:121–127. https://doi.org/10.3724/SP.J.1047.2008.00121

    Article  Google Scholar 

  41. Lv Z, Liu T, Benediktsson JA, Lei T, Wan Y (2018) Multi-Scale Object Histogram Distance for LCCD Using Bi-Temporal Very-High-Resolution Remote Sensing Images. Remote Sens 10(11). https://doi.org/10.3390/rs10111809

  42. Lv Z, Liu T, Zhang P, Benediktsson JA, Lei T, Zhang X (2019) Novel adaptive histogram trend similarity approach for land cover change detection by using bitemporal very-high-resolution remote sensing images. IEEE Trans Geosci Remote Sens 57(12):9554–9574

    Article  Google Scholar 

  43. Mallupattu P, Reddy JS (2013) Analysis of land use/land cover changes using remote sensing data and GIS at an Urban Area, Tirupati, India. Sci World J:1–6

  44. Malmir M, Zarkesh MMK, Monavari SM, Jozi SA, Sharifi E (2015) Urban development change detection based on multi-temporal satellite images as a fast tracking approach-A case study of Ahwaz county, southwestern Iran. Environ Monit Assess 187(3):108

    Article  Google Scholar 

  45. Mas J-F, Lemoine-Rodríguez R, González-López R, López-Sánchez J, Piña-Garduño A, Herrera-Flores E (2017) Land use/land cover change detection combining automatic processing and visual interpretation. Eur J Remote Sens 50:626–635

    Article  Google Scholar 

  46. Mishra PK, Rai A, Rai SC (2019) Land use and land cover change detection using geospatial techniques in the Sikkim Himalaya, India. Egypt J Remote Sens Space Sci 23(2):133–143

    Google Scholar 

  47. Möller M, Lymburner L, Volk M (2007) The Comparison Index: A Tool for Assessing the Accuracy of Image Segmentation. Int J Appl Earth Obs Geoinf 9(3):311–321. https://doi.org/10.1016/j.jag.2006.10.002

    Article  Google Scholar 

  48. Ojala T, Pietikäinen M, Mäenpää T (2002) Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns. IEEE Trans Pattern Anal Mach Intell 24(7):971–987

    Article  Google Scholar 

  49. Patia C, Pandaa AK, Tripathy AK, Pradhana SK, Patnaik S (2020) A novel hybrid machine learning approach for change detection in remote sensing images. Eng Sci Technol Int J 23(5):973–981. https://doi.org/10.1016/j.jestch.2020.01.002

    Article  Google Scholar 

  50. Peng D, Zhang Y (2017) Object-based change detection from satellite imagery by segmentation optimization and multi-features fusion. Int J Remote Sens 38(13):3886–3905. https://doi.org/10.1080/01431161.2017.1308033

    Article  Google Scholar 

  51. Peng D, Zhang Y (2017) Object-based change detection method using refined Markov random field. J Appl Remote Sens 11(1):016024

    Article  Google Scholar 

  52. Peng D, Zhang Y, Guan H (2019) End-to-End change detection for high resolution satellite images using improved UNet++. Remote Sens 11(11):1382

    Article  Google Scholar 

  53. Salazar A, Baldi G, Hirota M, Syktus J, Mcalpine C (2015) Land use and land cover change impacts on the regional climate of non-Amazonian South America: A review. Glob Planet Chang 128. https://doi.org/10.1016/j.gloplacha.2015.02.009

  54. Saremi S, Mirjalili S, Lewis A (2017) Grasshopper optimisation algorithm: theory and application. Adv Eng Softw 105:30–47

    Article  Google Scholar 

  55. ShahHosseini R, Homayouni S, Safari A (2015) Environmental monitoring based on automatic change detection from remotely sensed data: kernel-based approach. J Appl Remote Sens 9(1). https://doi.org/10.1117/1.JRS.9.095992

  56. Singh A (1989) Review Article Digital change detection techniques using remotely- sensed data. Int J Remote Sens 10:989–1003

    Article  Google Scholar 

  57. Tamilenthi S, Baskaran R (2013) Urban Change Detection Based on Remote Sensing and GIS Study of Salem Revenue Division, Salem District, Tamil Nadu, India. Eur Assoc Geogr 4(3):50–59

    Google Scholar 

  58. Tan K, Zhang Y, Wang X, Chen Y (2019) Object-Based Change Detection Using Multiple Classifiers and Multi-Scale Uncertainty Analysis. Remote Sens 11(3). https://doi.org/10.3390/rs11030359

  59. Thonfeld F, Feilhauer H, Braun M, Menz G (2016) Robust Change Vector Analysis (RCVA) for multi-sensor very high resolution optical satellite data. Int J Appl Earth Obs Geoinf 50:131–140. https://doi.org/10.1016/j.jag.2016.03.009

    Article  Google Scholar 

  60. Thunig H, Michel U, Ehlers M, Reinartz P (2011) Object-based rapid change detection for disaster management. Proceedings of SPIE - The International Society for Optical Engineering

  61. Tolessa T, Senbeta F, Kidane M (2017) The impact of land use/land cover change on ecosystem services in the central highlands of Ethiopia. Ecosyst Serv 23:47–54

    Article  Google Scholar 

  62. Tremeau A, Borel N (1997) A region growing and merging algorithm to color segmentation. Pattern Recogn 30(7):1191–1203

    Article  Google Scholar 

  63. Van De Weijer J, Gevers T (2004) Robust optical flow from photometric invariants. In Proceedings of the 2004 International Conference on Image Processing, ICIP 1835–1838

  64. Venugopal N (2019) Sample selection based change detection with dilated network learning in remote sensing images. Sens Imaging 20(1)

  65. Wang X, Liu S, Du P, Liang H, Xia J, Li Y (2018) Object-based change detection in urban areas from high spatial resolution images based on multiple features and ensemble learning. Remote Sens 10(2). https://doi.org/10.3390/rs10020276

  66. Wang D, Chen X, Jiang M, Du S, Xu B, Wang J (2021) ADS-Net: An Attention-Based deeply supervised network for remote sensing image change detection. Int J Appl Earth Obs Geoinf 101:102348. https://doi.org/10.1016/j.jag.2021.102348

    Article  Google Scholar 

  67. Wei H, Jinliang H, Lihui W, Yanxia H, Pengpeng H (2016) Remote sensing image change detection based on change vector analysis of PCA component. Remote Sens Land Resour 28(1):22–27. https://doi.org/10.6046/gtzyyg.2016.01.04

    Article  Google Scholar 

  68. Wu L, Zhang Z, Wang Y, Liu Q (2014) A segmentation based change detection method for high resolution remote sensing image. Pattern Recogn 483:314–324

    Google Scholar 

  69. Wu L, Wang Y, Long J, Liu Z (2015) A non-seed-based region growing algorithm for high resolution remote sensing image segmentation. In: Zhang YJ (ed) Image and Graphics. ICIG 2015. Lecture Notes in Computer Science. Springer, pp 263–277

    Google Scholar 

  70. Zhang YJ (1997) Evaluation and Comparison of Different Segmentation Algorithms. Pattern Recogn Lett 18:963–974

    Article  Google Scholar 

  71. Zhang Z, Vosselman G, Gerke M, Tuia D, Yang MY (2018) Change detection between multimodal remote sensing data using Siamese CNN. arXiv 2018, arXiv:1807.09562

  72. Zhang Y, Peng D, Huang X (2018) Object-Based Change Detection for VHR Images Based on Multiscale Uncertainty Analysis. IEEE Geosci Remote Sens Lett 15(1):13–17. https://doi.org/10.1109/LGRS.2017.2763182

    Article  Google Scholar 

  73. Zhang C, Yue P, Tapete D, Jiang L, Shangguan B, Huang L, Liu G (2020) A deeply supervised image fusion network for change detection in high resolution bi-temporal remote sensing images. ISPRS J Photogramm Remote Sens 166:183–200

    Article  Google Scholar 

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  1. Mayur Vaid and Shivam Gupta contributed equally to this work.

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  1. Department of Information Technology, Netaji Subhas University of Technology, Dwarka, New Delhi, India

    Priti Bansal, Mayur Vaid & Shivam Gupta

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Bansal, P., Vaid, M. & Gupta, S. OBCD-HH: an object-based change detection approach using multi-feature non-seed-based region growing segmentation. Multimed Tools Appl 81, 8059–8091 (2022). https://doi.org/10.1007/s11042-021-11779-y

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