ارائه فیلتری محلی جهت بهبود مدل رقومی ارتفاعی زمین
محورهای موضوعی : توسعه سیستم های مکانی
محمدامین قنادی
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متین شهری
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1 - گروه مهندسی نقشه برداری، دانشکده مهندسی علوم زمین، دانشگاه صنعتی اراک
2 - استادیار گروه مهندسی نقشه برداری، دانشکده مهندسی علوم زمین، دانشگاه صنعتی اراک
کلید واژه: حذف اشتباهات, مدل رقومی ارتفاعی زمین, فیلتر انطباق پذیر, بهبود مدل رقومی ارتفاعی,
چکیده مقاله :
مدل رقومی ارتفاعی دقیق و با کیفیت از سطح زمین برای بسیاری از کاربردها ضروری می‌باشد. به علت وجود برخی مشکلات در برداشت داده و همچنین ضعف در تکنیک‌های تولید مدل رقومی ارتفاعی از جمله روش‌های درونیابی، این مدل‌ها با خطاهایی بعضا بزرگ همراه می-باشند که می‌بایست بصورت دستی و یا اتوماتیک ویرایش شوند. در این مطالعه یک روش جهت حذف خطاهای بزرگ و همچنین بازسازی مدل رقومی ارتفاعی زمین پیشنهاد شده است. در این روش دو مرحله‌ای ابتدا با استفاده از انحراف معیار فیلتر کوتاه شده آلفا، نقاط با خطای بزرگ شناسایی و حذف می‌شوند. در ادامه یک فیلتر وزن‌دهی با استفاده از معکوس فاصله که انطباق پذیر است با هدف حذف اشتباهات و بازسازی مدل رقومی ارتفاعی زمین اجرا می‌شود. روش پیشنهادی و چند روش رقابتی معمول بر روی یک مدل رقومی ارتفاعی شبیه‌سازی شده و یک مدل رقومی ارتفاعی یک متری مستخرج از تصاویر استریو ماهواره‌ای از جنوب غرب شهرستان مشهد اجرا شده‌اند و مورد ارزیابی قرار گرفته‌اند. خطای جذر میانگین مربعات مدل رقومی تصحیح شده مستخرج از تصاویر ماهواره‌ای با استفاده از روش پیشنهادی 1.89 متر است و این در حالی است که این شاخص برای روش فیلتر وزن‌دهی با استفاده از معکوس فاصله معمول 2.43 متر می‌باشد. نتایج آزمایش‌ها نشان می‌دهد که روش پیشنهادی علیرغم افزایش 19% هزینه زمانی می‌تواند دقت مدل رقومی ارتفاعی اصلاح شده را در قیاس با روش وزن-دهی با استفاده از معکوس فاصله معمول حداقل 22% بهبود دهد. بنابراین می‌توان از فیلتر پیشنهاد شده در این مطالعه در حذف نویز و بهبود مدل رقومی ارتفاعی زمین در شرایطی که افزایش دقت در الویت است استفاده نمود.
An accurate, high-quality Digital Elevation Model (DEM) from the ground is essential for many applications. Due to some data collection problems as well as weaknesses in DEM production techniques, including interpolation methods, these models are associated with some blunder errors that must be edited manually or automatically. In this study, a method for removing noise and blunders as well as improving the DEM is proposed. In this two-step method, first, with the standard deviation of the alpha-trimmed filter, points with blunders are identified and removed. Then, an adaptive inverse distance weighted filter is applied to remove blunders and refine the DEM. The proposed method and some common competitive methods have been applied and evaluated on a simulated DEM and a DEM extracted from satellite stereo images from the southwest of Mashhad city. The root means square error of the DEM extracted from satellite images using the proposed method is 1.89 meters, while this criteria for the inverse distance weighted filter method is 2.43 meters. The experimental results show that the proposed method, despite a 20% increase in time cost, can improve the accuracy of the modified DEM by at least 22% compared to the weighting method using the inverse distance. Therefore, the filter proposed in this study can be used to remove noise and improve DEM when increasing the accuracy is a priority.
1. Polidori L, El Hage M. 2020. Digital elevation model quality assessment methods: A critical review. Remote sensing, 12(21): 3522.
2. Gharib Bafghi Z, Tian J, d'Angelo P, Reinartz P. 2016. A new algorithm for void filling in a DSM from stereo satellite images in urban areas. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2016, 3: 55-61.
3. Bhushan S, Shean D, Alexandrov O, Henderson S. 2021. Automated digital elevation model (DEM) generation from very-high-resolution Planet SkySat triplet stereo and video imagery. ISPRS Journal of Photogrammetry and Remote Sensing, 173: 151-165.
4. Silva Id. 2020. Geomatics Applied to Civil Engineering State of the Art. In: Applications of Geomatics in Civil Engineering. Springer, pp 31-46.
5. Fijałkowska A. 2021. Analysis of the influence of DTM source data on the LS factors of the soil water erosion model values with the use of GIS technology. Remote Sensing, 13(4): 678.
6. Bagheri M, Jelokhani Noaryki M, Bagheri K. 2018. Investigation of the land potential of Kermanshah province for rainfed wheat cultivation using artificial neural network. Journal of RS and GIS for Natural Resources, 8(4): 36-48. (In Persian).
7. Scheip CM. 2021. Integrating water-classified returns in DTM generation to increase accuracy of stream delineations and geomorphic analyses. Geomorphology, 385: 107722.
8. Erskine J, Oxendine C, Wright W, O'banion M, Philips A. 2022. Evaluating the relationship between data resolution and the accuracy of identified helicopter landing zones (HLZs). Applied Geography, 139: 102652.
9. Schreyer J, Walker BB, Lakes T. 2022. Implementing urban canopy height derived from a TanDEM-X-DEM: An expert survey and case study. ISPRS Journal of Photogrammetry and Remote Sensing, 187: 345-361.
10. Polat N, Uysal M. 2018. An experimental analysis of digital elevation models generated with Lidar Data and UAV photogrammetry. Journal of the Indian Society of Remote Sensing, 46(7): 1135-1142.
11. Sudalaimuthu K, Jesudhas CJ, Ramachandran U, Somanathan AK, Ganapathy S, Jeyakumar RB. 2022. Development of digital elevation model for assessment of flood vulnerable areas using Cartosat‐1 and GIS at Thamirabarani river, Tamilnadu, India. Environmental Quality Management.
12. Jawecki B, Szewrański S, Stodolak R, Wang Z. 2019. The use of digital terrain models to estimate the pace of filling the pit of a central European granite quarry with water. Water, 11(11): 2298.
13. Singh S, Vinod Kumar K, Jagannadha Rao M. 2020. Utilization of LiDAR DTM for systematic improvement in mapping and classification of coastal micro-geomorphology. Journal of the Indian Society of Remote Sensing, 48(5): 805-816.
14. Irvem A. 2011. Application of GIS to determine storage volume and surface area of reservoirs: the case study of Buyuk Karacay dam. Int J Nat Eng Sci, 5: 39-43.
15. Liu X, Wang N, Shao J, Chu X. 2017. An automated processing algorithm for flat areas resulting from DEM filling and interpolation. ISPRS International Journal of Geo-Information, 6(11): 376.
16. Niipele JN, Chen J. 2019. The usefulness of alos-palsar dem data for drainage extraction in semi-arid environments in The Iishana sub-basin. Journal of Hydrology: Regional Studies, 21: 57-67.
17. Ibrahim M, Al-Mashaqbah A, Koch B, Datta P. 2020. An evaluation of available digital elevation models (DEMs) for geomorphological feature analysis. Environmental Earth Sciences, 79(13): 1-11.
18. Zali M, Solaimani K, Habibnejad Roshan M, Miryaghoubzadeh MH. 2022. Comparison and prioritization of flooding in Nekarood sub-basins using morphometric method in GIS. Journal of RS and GIS for Natural Resources, 13(2): 6-10. (In Persian).
19. Guth P, Kane M. 2021. Slope, aspect, and hillshade algorithms for non‐square digital elevation models. Transactions in GIS, 25(5): 2309-2332.
20. Toutin T. 2011. State-of-the-art of geometric correction of remote sensing data: a data fusion perspective. International Journal of Image and Data Fusion, 2(1): 3-35.
21. Depountis N, Michalopoulou M, Kavoura K, Nikolakopoulos K, Sabatakakis N. 2020. Estimating soil erosion rate changes in areas affected by wildfires. ISPRS International Journal of Geo-Information, 9(10): 562.
22. Handayani HH, Bawasir A, Cahyono AB, Hariyanto T, Hidayat H. 2022. Surface drainage features identification using LiDAR DEM smoothing in agriculture area: a study case of Kebumen Regency, Indonesia. International Journal of Image and Data Fusion: 1-22.
23. Zhan D, Kwan M-P, Zhang W, Yu X, Meng B, Liu Q. 2018. The driving factors of air quality index in China. Journal of Cleaner Production, 197: 1342-1351.
24. Achilleos G. 2011. The Inverse Distance Weighted interpolation method and error propagation mechanism–creating a DEM from an analogue topographical map. Journal of spatial Science, 56(2): 283-304.
25. Chaplot V, Darboux F, Bourennane H, Leguédois S, Silvera N, Phachomphon K. 2006. Accuracy of interpolation techniques for the derivation of digital elevation models in relation to landform types and data density. Geomorphology, 77(1-2): 126-141.
26. Arun PV. 2013. A comparative analysis of different DEM interpolation methods. The Egyptian Journal of Remote Sensing and Space Science, 16(2): 133-139.
27. Salekin S, Burgess JH, Morgenroth J, Mason EG, Meason DF. 2018. A comparative study of three non-geostatistical methods for optimising digital elevation model interpolation. ISPRS international journal of geo-information, 7(8): 300.
28. Ajvazi B, Czimber K. 2019. A comparative analysis of different DEM interpolation methods in GIS: case study of Rahovec, Kosovo. Geodesy and cartography, 45(1): 43-48.
29. Bater CW, Coops NC. 2009. Evaluating error associated with lidar-derived DEM interpolation. Computers & Geosciences, 35(2): 289-300.
30. Stereńczak K, Ciesielski M, Balazy R, Zawiła-Niedźwiecki T. 2016. Comparison of various algorithms for DTM interpolation from LIDAR data in dense mountain forests. European Journal of Remote Sensing, 49(1): 599-621.
31. Chen C, Li Y, Zhao N, Yan C. 2017. Robust interpolation of DEMs from LiDAR-derived elevation data. IEEE Transactions on Geoscience and Remote Sensing, 56(2): 1059-1068.
32. Heritage GL, Milan DJ, Large AR, Fuller IC. 2009. Influence of survey strategy and interpolation model on DEM quality. Geomorphology, 112(3-4): 334-344.
33. Giribabu D, Rao SS, Murthy YK. 2013. Improving Cartosat-1 DEM accuracy using synthetic stereo pair and triplet. ISPRS journal of photogrammetry and remote sensing, 77: 31-43.
34. Kim S, Rhee S, Kim T. 2018. Digital surface model interpolation based on 3D mesh models. Remote Sensing, 11(1): 24.
35. Ghannadi MA, Alebooye S, Izadi M, Moradi A. 2020. A method for Sentinel-1 DEM outlier removal using 2-D Kalman filter. Geocarto International: 1-15.
36. Mukherjee S, Joshi PK, Mukherjee S, Ghosh A, Garg R, Mukhopadhyay A. 2013. Evaluation of vertical accuracy of open source Digital Elevation Model (DEM). International Journal of Applied Earth Observation and Geoinformation, 21: 205-217.
37. Moradi AR, Ghannadi MA. 2020. Presenting a method for the improvement of Sentinel-1 generated DEM, using SRTM and 2D wavelet transform. Scientific-Research Quarterly of Geographical Data (SEPEHR), 29(115): 35-48.
38. Zhu Y, Liu X, Zhao J, Cao J, Wang X, Li D. 2019. Effect of DEM interpolation neighbourhood on terrain factors. ISPRS International Journal of Geo-Information, 8(1): 30.
39. Gharibbafghi Z, Tian J, Reinartz P. 2018. Modified superpixel segmentation for digital surface model refinement and building extraction from satellite stereo imagery. Remote Sensing, 10(11): 1824.
40. Bednar J, Watt T. 1984. Alpha-trimmed means and their relationship to median filters. IEEE Transactions on acoustics, speech, and signal processing, 32(1): 145-153.
41. Esfandiari F, Ghorbani Filabadi R, Nasiri Khiavi A, Mostafazadeh R. 2019. Assessing the accuracy of algebraic and geostatistical techniques to determine the spatial variations of groundwater quality in Boroojen Plain. Journal of Natural Environmental Hazards, 8(20): 115-130. (In Persian).
42. Asghari Saraskanroud S, Ghale E, Ebady E. 2021. Investigation of land use changes and its relationship with groundwater level (Case study: Ardabil plain). Journal of RS and GIS for Natural Resources, 12(1): 86-106. (In Persian).
43. Ghannadi M, Saadatseresht M, Motagh M. 2015. Sentinel-1 Image Matching Using Strong Scatters. ISPRS-International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XL-1-W5: 233-235.