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  • مروری بر روش های آشکارسازی دوبعدی و سه بعدی تغییرات ساختمان مبتنی بر داده‌های سنجش از دور

اشتراک گذاری

آدرس مقاله


کد مقاله : JEST-2001-4976 (R2) بازدید : 72 صفحه: 191 - 206

10.30495/jest.2022.50484.4976

نوع مقاله: پژوهشی

مروری بر روش های آشکارسازی دوبعدی و سه بعدی تغییرات ساختمان مبتنی بر داده‌های سنجش از دور

محورهای موضوعی : محیط زیست شهری

شراره حسینی 1 (کارشناسی ارشد، گروه مهندسی نقشه برداری، دانشکده مهندسی عمران، دانشگاه تربیت دبیر شهید رجایی، تهران، ایران.)
فاطمه طبیب محمودی 2 (استادیار، گروه مهندسی نقشه برداری، دانشکده مهندسی عمران، دانشگاه تربیت دبیر شهید رجایی، تهران، ایران. *(مسوول مکاتبات))

تاریخ دریافت : 1398/11/01 تاریخ پذیرش : 1399/05/11 تاریخ انتشار : 1400/07/01

کلید واژه: اصلاح هندسی, آشکارسازی تغییرات, ساختمان, روش پیکسل مبنا و شیء مبنا, سنجش از دور, پس طبقه بندی,

چکیده مقاله :

زمینه و هدف: در مواجهه با ساختمان ها و ابنیه شهری، توسعه پایدار شهر و لزوم به هنگام نگه داشتن مدل‌‌های سه‌‌بعدی آن نیاز به پایش مداوم تغییرات رخ داده در سطح زمین دارد. بنابراین، آشکار‌سازی تغییرات کاربری و یا پوشش زمین با استفاده از داده‌‌های سنجش از دور چند‌زمانه برای درک روابط پویا بین پدیده‌‌های انسانی و طبیعی به منظور تصمیم‌‌گیری صحیح و به تبع آن تسهیل مدیریت شهری و بحران و استفاده بهینه از منابع، بسیار حائز اهمیت است. هدف از انجام این مطالعه مروری، پاسخ به این سوال است که آیا پیشرفت های صورت گرفته در روش های آشکارسازی تغییرات ساختمان ها و تبدیل آنها از روش های دوبعدی به روش های سه بعدی توانسته چالش های مطرح در این زمینه را پاسخ دهد و زمینه های تحقیقاتی آتی برای بهبود نتایج روش های سه بعدی آشکارسازی تغییرات چیست؟ روش بررسی: در این مقاله، مروری کلی بر دسته بندی انواع روش های مطرح در آشکارسازی تغییرات عوارض شهری با تاکید بر عارضه ساختمان انجام شده است. پیشرفت های موجود در تسهیل دستیابی به داده های سه بعدی موجب شده روش های آشکارسازی سه بعدی تغییرات با دو مفهوم مقایسه هندسی و آنالیز هندسی-طیفی مورد توجه تحقیقاتی قرار گیرند. یافته ها: با توجه به مطالعات صورت گرفته در زمینه آشکارسازی دوبعدی تغییرات با استفاده از روش‌های مختلف، دقت نتایج وابسته به پاره‌ای از عوامل نظیر قدرت تفکیک مکانی و رادیومتریکی داده های سنجش از دور ورودی، رفتار پدیده‌ها در منطقه مورد مطالعه و چرخه تغییرات طبیعی در آن‌ها، تکنیک‌های به کار رفته در شناسایی تغییرات، تجربه و مهارت عامل خبره و ... می‌باشد. امروزه به داده‌های سه‌بعدی دسترسی آسان‌تری داریم. آشکارسازی سه بعدی تغییرات با استفاده از داده های سنجش از دور افزونه‌ای از تحقیقات بسیار کلاسیک و در عین حال رایج است که در آن از اطلاعات سه‌بعدی در فرآیند تشخیص تغییر استفاده می‌شود. نتیجه گیری: در بررسی الگوریتم آشکارسازی دو بعدی و سه بعدی تغییرات، این نتیجه به دست می آید که در اکثر موارد، روش‌های آشکارسازی سه بعدی تغییرات به شدت به دو موضوع اساسی متکی هستند: 1) استفاده از الگوریتم های پیشرفته تناظریابی تصاویر برای تولید داده‌های سه‌بعدی؛ 2) تکنیک‌های استخراج و یادگیری ماشین سطح بالا براساس داده‌های هندسی و طیفی. بنابراین، جدا از الگوریتم آشکارسازی تغییر، توسعه تکنیک‌های آشکارسازی سه بعدی تغییرات بستگی به تلاش تحقیقاتی در این دو جنبه دارد.

چکیده انگلیسی:

Background and Objective: Land is rapidly changing at the local, regional, national, and global scales, with a significant impact on the environment. Some changes occur due to natural causes, while other changes due to human projects such as urban growth. Material and Methodology: This article provides an overview on the categorization of different methods used in detecting urban changes with emphasis on building complexities. Advances in facilitating the acquisition of three-dimensional data have led to three-dimensional change detection methods with two concepts of geometric comparison (including height difference calculation, Euclidean distance and transition-based methods) and geometric-spectral analysis (including correction). The purpose of this review is to answer the question of whether advances in change detecion methods and converting them from two-dimensional methods to three-dimensional ones have been able to meet the challenges in this context. What is future research to improve the results of 3D change detection methods? Findings: According to the results of research on different types of change detection methods, although two-dimensional change detection methods have considerable variation, they lack altitude information and estimation of changes in the third dimension and in the face of high spatial and spectral resolution and three-dimensional effects such as buildings face challenges. Therefore, just by relying on the results of these methods, it is not possible to get a proper assessment of damages during accidents or construction estimations and so on. Discussion and Conclusion: In this article, while discussing the concepts presented in the three-dimensional methods of detecting changes, the strengths and weaknesses and challenges of the existing research are compared with the two-dimensional methods. It is concluded that in most cases, three-dimensional change detection methods rely heavily on two basic issues: 1) the use of advanced image-matching algorithms to produce three-dimensional data; 2) high-level machine learning techniques based on geometric and spectral data.

منابع و مأخذ:


  1. Qin, R. 2014. Change detection on LOD 2 building models with very high resolution space-borne stereo imagery. ISPRS Journal of Photogrammetry and Remote Sensing 96 (2014): PP.179-192.
    2.
  2. Singh, A.1989. Digital Change Detection Techniques Using Remotely-Sensed Data. International Journal of Remote Sensing 10 (6): PP. 989-1003.
    3.

3.      Madanian, M. Safianian, A. 2012. A review of some of the methods for detecting changes using remote sensing data. Geographical information journal (Sepehr), Vol. 21 (82): PP. 44-49 (In Persian)

  1. Lu, D. Mausel, P.   Brondzio, E. and Moran, E. 2004. Change Detection Techniques. International Journal of Remote Sensing, Vol. 25 (12): PP. 2365–2407.
    2.
  2. Lu, D. Mausel, P.   Brondzio, E. and Moran, E. 2002. Change detection of successional and mature forests based on forest stand characteristics using multitemporal TM data in Altamira, Brazil. In: XXII FIG International Congress, ACSM–ASPRS Annual Conference Proceedings, Washington, DC, USA, pp. 19-26.
    3.
  3. Brunner, D. Lemoine, G. and Bruzzone, L. 2010. Earthquake damage assessment of buildings using VHR optical and SAR imagery. IEEE Transactions on Geoscience and Remote Sensing, Vol. 48 (5), PP. 2403-2420.
    4.
  4. Huang, X.  Zhang, L. and Zhu, T. 2014. Building change detection from multitemporal high-resolution remotely sensed images based on a morphological building index. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol. 7 (1), PP.105-115.
    5.
  5. Košecka, J. 2012. Detecting changes in images of street scenes.” In: Asian Conference on Computer Vision, PP. 590-601.
    6.
  6. Vakalopoulou, M. Karantzalos, K. Komodakis, N. and Paragios, N. 2015. Simultaneous registration and change detection in multitemporal, very high resolution remote sensing data. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 61-69. 
    7.
  7. Tian, J. Dezert, J.and Qin, R. 2018. Time-series 3D Building Change based on Belief Functions.
    8.
  8. Qin, R. Tian, J. and Reinartz, P. 2016. 3D change detection – Approaches and applications. ISPRS Journal of Photogrammetry and Remote Sensing. DOI: 10.1016/j.isprsjprs.2016.09.013.
    9.
  9. Hussain, M. Chen, D. Cheng, A. Wei, H. and Stanley, D. 2013. Change detection from remotely sensed images: From pixel-based to object-based approaches, ISPRS Journal of Photogrammetry and Remote Sensing, vol. 80: pp. 91-106. 
    10.
  10. Moghimi, A. Ebadi, H. Sadeghi, V. 2016. Review on the pixel based and object based change detection methods on the multi-temporal satellite images.Geospatial engineering journal, Vol. 7 (2): PP. 99-110. (In Persian)
    11.
  11. Coppin, P. Jonckheere, I. Nackaerts, K. Muys, B. and Lambin, E. 2004. Review ArticleDigital change detection methods in ecosystem monitoring: a review, International journal of remote sensing, vol. 25, pp. 1565-1596.
    12.
  12. Jianya, G. Haigang, S. Guorui, M. and Qiming, Z. 2008. A review of multi-temporal remote sensing data change detection algorithms," The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. 37, pp. 757-762.
    13.
  13. Turker, M. and B. Cetinkaya, 2005. Automatic detection of earthquake‐damaged buildings using DEMs created from preand post‐earthquake stereo aerial photographs. International Journal of Remote Sensing 26 (4), PP.823-832.
    14.
  14. Tian, J., Reinartz, P., d’ Angelo, P. and Ehlers, M., 2013. Region-based automatic building and forest change detection on cartosat-1 stereo imagery. ISPRS Journal of Photogrammetry and Remote Sensing 79(0), pp. 226 – 239.
    15.
  15. Tian, J., H. Chaabouni-Chouayakh, P. Reinartz, T. Krauß and P. d'Angelo, 2010. Automatic 3D change detection based on optical satellite stereo imagery. International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences 38 (Part 7B), PP. 586-591.
    16.
  16. Gruen, A. and Akca, D. 2005. Least squares 3D surface and curve matching. ISPRS Journal of Photogrammetry and Remote Sensing 59 (3), PP. 151-174.
    17.
  17. Waser, L. Baltsavias, E. Ecker, K. Eisenbeiss, H. Feldmeyer-Christe, E. Ginzler, C. Küchler, M. and Zhang, L. 2008. Assessing changes of forest area and shrub encroachment in a mire ecosystem using digital surface models and CIR aerial images. Remote Sensing of Environment 112 (5), PP.1956-1968.
    18.
  18. Akca, D., M. Freeman, I. Sargent and A. Gruen, 2010. Quality assessment of 3D building data. The Photogrammetric Record 25 (132), PP.339-355.
    19.
  19. Eden, I. and D. B. Cooper, 2008. Using 3D line segments for robust and efficient change detection from multiple noisy images. In: 10th European Conference on Computer Vision, Marseille, France, 12-18, October, PP. 172-185.
    20.
  20. Champion, N., D. Boldo, M. Pierrot-Deseilligny and G. Stamon, 2010. 2D building change detection from high resolution satelliteimagery: A two-step hierarchical method based on 3D invariant primitives. Pattern Recognition Letters 31 (10), PP.1138-1147.
    21.
  21. Knudsen, T. and B. P. Olsen, 2003. Automated change detection for updates of digital map databases. Photogrammetric Engineering and Remote Sensing 69 (11), 1289-1296.
    22.
  22. Qin, R. and A. Gruen, 2014. 3D change detection at street level using mobile laser scanning point clouds and terrestrial images. ISPRS Journal of Photogrammetry and Remote Sensing 90 (2014), 23-35.
    23.
  23. Dini, G., K. Jacobsen, F. Rottensteiner, M. Al Rajhi and C. Heipke, 2012. 3D Building Change Detection Using High Resolution Stereo Images and a GIS Database. ISPRS-International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 1 299-304.
    24.
  24. Choi, K., I. Lee and S. Kim, 2009. A feature based approach to automatic change detection from Lidar data in urban areas. International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences 38 (Part 3/W8), 259-264.
    25.
  25. Guerin, C., R. Binet and M. Pierrot-Deseilligny, 2014. Automatic Detection of Elevation Changes by Differential DSM Analysis: Application to Urban Areas. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 7 (10), 4020-4037.
    26.
  26. Tian, J., A. A. Nielsen and P. Reinartz, 2014b. Improving change detection in forest areas based on stereo panchromatic imagery using kernel MNF. IEEE Transactions on Geoscience and Remote Sensing 52 (11), 7130 - 7139.
    27.
  27. Vögtle, T. and E. Steinle, 2004. Detection and recognition of changes in building geometry derived from multitemporal laserscanning data. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 35 (B2), 428-433.
    28.
  28. Teo, T.-A. And T.-Y. Shih, 2013. LiDAR-based change detection and change-type determination in urban areas. International Journal of Remote Sensing 34 (3), 968-981.
    29.
  29. Qin, R., X. Huang, A. Gruen and G. Schmitt, 2015a. Object-Based 3-D Building Change Detection on Multitemporal Stereo Images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 5 (8), 2125-2137.10.1109/JSTARS.2015.2424275.  
    30.

 

 





 

 

 

 

 

 

 

 

 

 

 

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  1. Qin, R. 2014. Change detection on LOD 2 building models with very high resolution space-borne stereo imagery. ISPRS Journal of Photogrammetry and Remote Sensing 96 (2014): PP.179-192.
    2.
  2. Singh, A.1989. Digital Change Detection Techniques Using Remotely-Sensed Data. International Journal of Remote Sensing 10 (6): PP. 989-1003.
    3.

3.      Madanian, M. Safianian, A. 2012. A review of some of the methods for detecting changes using remote sensing data. Geographical information journal (Sepehr), Vol. 21 (82): PP. 44-49 (In Persian)

  1. Lu, D. Mausel, P.   Brondzio, E. and Moran, E. 2004. Change Detection Techniques. International Journal of Remote Sensing, Vol. 25 (12): PP. 2365–2407.
    2.
  2. Lu, D. Mausel, P.   Brondzio, E. and Moran, E. 2002. Change detection of successional and mature forests based on forest stand characteristics using multitemporal TM data in Altamira, Brazil. In: XXII FIG International Congress, ACSM–ASPRS Annual Conference Proceedings, Washington, DC, USA, pp. 19-26.
    3.
  3. Brunner, D. Lemoine, G. and Bruzzone, L. 2010. Earthquake damage assessment of buildings using VHR optical and SAR imagery. IEEE Transactions on Geoscience and Remote Sensing, Vol. 48 (5), PP. 2403-2420.
    4.
  4. Huang, X.  Zhang, L. and Zhu, T. 2014. Building change detection from multitemporal high-resolution remotely sensed images based on a morphological building index. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol. 7 (1), PP.105-115.
    5.
  5. Košecka, J. 2012. Detecting changes in images of street scenes.” In: Asian Conference on Computer Vision, PP. 590-601.
    6.
  6. Vakalopoulou, M. Karantzalos, K. Komodakis, N. and Paragios, N. 2015. Simultaneous registration and change detection in multitemporal, very high resolution remote sensing data. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 61-69. 
    7.
  7. Tian, J. Dezert, J.and Qin, R. 2018. Time-series 3D Building Change based on Belief Functions.
    8.
  8. Qin, R. Tian, J. and Reinartz, P. 2016. 3D change detection – Approaches and applications. ISPRS Journal of Photogrammetry and Remote Sensing. DOI: 10.1016/j.isprsjprs.2016.09.013.
    9.
  9. Hussain, M. Chen, D. Cheng, A. Wei, H. and Stanley, D. 2013. Change detection from remotely sensed images: From pixel-based to object-based approaches, ISPRS Journal of Photogrammetry and Remote Sensing, vol. 80: pp. 91-106. 
    10.
  10. Moghimi, A. Ebadi, H. Sadeghi, V. 2016. Review on the pixel based and object based change detection methods on the multi-temporal satellite images.Geospatial engineering journal, Vol. 7 (2): PP. 99-110. (In Persian)
    11.
  11. Coppin, P. Jonckheere, I. Nackaerts, K. Muys, B. and Lambin, E. 2004. Review ArticleDigital change detection methods in ecosystem monitoring: a review, International journal of remote sensing, vol. 25, pp. 1565-1596.
    12.
  12. Jianya, G. Haigang, S. Guorui, M. and Qiming, Z. 2008. A review of multi-temporal remote sensing data change detection algorithms," The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. 37, pp. 757-762.
    13.
  13. Turker, M. and B. Cetinkaya, 2005. Automatic detection of earthquake‐damaged buildings using DEMs created from preand post‐earthquake stereo aerial photographs. International Journal of Remote Sensing 26 (4), PP.823-832.
    14.
  14. Tian, J., Reinartz, P., d’ Angelo, P. and Ehlers, M., 2013. Region-based automatic building and forest change detection on cartosat-1 stereo imagery. ISPRS Journal of Photogrammetry and Remote Sensing 79(0), pp. 226 – 239.
    15.
  15. Tian, J., H. Chaabouni-Chouayakh, P. Reinartz, T. Krauß and P. d'Angelo, 2010. Automatic 3D change detection based on optical satellite stereo imagery. International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences 38 (Part 7B), PP. 586-591.
    16.
  16. Gruen, A. and Akca, D. 2005. Least squares 3D surface and curve matching. ISPRS Journal of Photogrammetry and Remote Sensing 59 (3), PP. 151-174.
    17.
  17. Waser, L. Baltsavias, E. Ecker, K. Eisenbeiss, H. Feldmeyer-Christe, E. Ginzler, C. Küchler, M. and Zhang, L. 2008. Assessing changes of forest area and shrub encroachment in a mire ecosystem using digital surface models and CIR aerial images. Remote Sensing of Environment 112 (5), PP.1956-1968.
    18.
  18. Akca, D., M. Freeman, I. Sargent and A. Gruen, 2010. Quality assessment of 3D building data. The Photogrammetric Record 25 (132), PP.339-355.
    19.
  19. Eden, I. and D. B. Cooper, 2008. Using 3D line segments for robust and efficient change detection from multiple noisy images. In: 10th European Conference on Computer Vision, Marseille, France, 12-18, October, PP. 172-185.
    20.
  20. Champion, N., D. Boldo, M. Pierrot-Deseilligny and G. Stamon, 2010. 2D building change detection from high resolution satelliteimagery: A two-step hierarchical method based on 3D invariant primitives. Pattern Recognition Letters 31 (10), PP.1138-1147.
    21.
  21. Knudsen, T. and B. P. Olsen, 2003. Automated change detection for updates of digital map databases. Photogrammetric Engineering and Remote Sensing 69 (11), 1289-1296.
    22.
  22. Qin, R. and A. Gruen, 2014. 3D change detection at street level using mobile laser scanning point clouds and terrestrial images. ISPRS Journal of Photogrammetry and Remote Sensing 90 (2014), 23-35.
    23.
  23. Dini, G., K. Jacobsen, F. Rottensteiner, M. Al Rajhi and C. Heipke, 2012. 3D Building Change Detection Using High Resolution Stereo Images and a GIS Database. ISPRS-International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 1 299-304.
    24.
  24. Choi, K., I. Lee and S. Kim, 2009. A feature based approach to automatic change detection from Lidar data in urban areas. International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences 38 (Part 3/W8), 259-264.
    25.
  25. Guerin, C., R. Binet and M. Pierrot-Deseilligny, 2014. Automatic Detection of Elevation Changes by Differential DSM Analysis: Application to Urban Areas. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 7 (10), 4020-4037.
    26.
  26. Tian, J., A. A. Nielsen and P. Reinartz, 2014b. Improving change detection in forest areas based on stereo panchromatic imagery using kernel MNF. IEEE Transactions on Geoscience and Remote Sensing 52 (11), 7130 - 7139.
    27.
  27. Vögtle, T. and E. Steinle, 2004. Detection and recognition of changes in building geometry derived from multitemporal laserscanning data. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 35 (B2), 428-433.
    28.
  28. Teo, T.-A. And T.-Y. Shih, 2013. LiDAR-based change detection and change-type determination in urban areas. International Journal of Remote Sensing 34 (3), 968-981.
    29.
  29. Qin, R., X. Huang, A. Gruen and G. Schmitt, 2015a. Object-Based 3-D Building Change Detection on Multitemporal Stereo Images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 5 (8), 2125-2137.10.1109/JSTARS.2015.2424275.  
    30.

 

 





 

 

 

 

 

 

 

 

 

 

 

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