@article { author = {Ebrahimikia, Mojdeh and HosseiniNaveh, Ali}, title = {A new way to decrease the distortion of the edges of buildings on true orthophotos}, journal = {Scientific- Research Quarterly of Geographical Data (SEPEHR)}, volume = {31}, number = {123}, pages = {89-106}, year = {2022}, publisher = {National Geographical Organization}, issn = {2588-3860}, eissn = {2588-3879}, doi = {10.22131/sepehr.2022.699911}, abstract = {Extended Abstract  Introduction Today, orthophotos are one of the most widely used products in the field of spatial information, and they are often created from aerial or satellite images, so paying attention to their accuracy and quality is essential in order to have both geometric and radiometric information. The point clouds and the digital surface model used to build them are the two most important aspects that affect the quality of these images. On true orthophotos, there are some distortions on the structural edges of buildings, which is due to defects in these areas in the point cloud used in the digital surface model. This problem is greater for orthophotos that have been made from UAV images in urban areas because of their lower altitude. Additionally, because of the presence of occluded regions and radiometric changes between overlapping images, approaches for creating point clouds based on image matching are unable to produce complete point clouds and contain flaws, particularly towards the outer edges of objects with high height differences. Before interpolation of the point cloud and preparation of the digital surface model and then preparation of orthophotos of it, it is necessary to complete the point cloud in areas with defects. Some studies have shown that adding edge points has the effect of decreasing the distortion of true orthophotos. In this study, a new method for completing point clouds is proposed and explained in detail.  Materials & Methods In this study, the imaging of the Yazd region was done with a Phantom 4 drone equipped with a DJI camera. The SfM algorithm has been used to calibrate the camera, estimate the internal and external camera parameters, and produce images without distortion and low-density point clouds, and SGM has been used to produce dense point clouds. In the proposed method, the purpose is to complete the incomplete points of the building. Assuming that the points on the roof of each building are predetermined, without noise, and have incomplete edges, these point clouds were used to complete them, and then added to the existing point clouds in their actual coordinates. The final point cloud was used in the preparation of digital models to produce irregular and then regular surfaces and in the preparation of true orthophotos using camera parameters and undistorted images. One of the images with buildings marked as numbers 1 to 4 was selected to perform tests and prepare orthophotos. Results & Discussion The lack of structural edge points on any roof, which is the distance between severe height differences between levels, causes the greatest amount of distortion on the edge of the roof and around it. Adding these points with edge line recognition and reconstruction algorithms to the point cloud improves the resulting digital surface model. Since the quality and accuracy of the digital elevation model directly affects the resulting orthophoto, using a more accurate digital elevation model improves these images. These point clouds have been modified in the proposed method, and quantitative and qualitative comparisons demonstrate improved results in eliminating distortion in the majority of the buildings studied. The reasons for the superiority of the proposed method over previous methods include determining and calculating a more complete and precise form of the roof of each building and considering the outermost edges of the buildings.    Conclusion The biggest amount of distortion on the edge of the roofs and their surroundings is caused by the lack of points on the structural edge of each roof, which is the boundary between dramatic height variations between the levels. By integrating these points with algorithms for recognizing and repairing edge lines, the resulting digital elevation model will be improved. This study presented a new method for completing the point cloud that enhanced the quality of true orthophoto edges, which was tested on a large number of building images and compared to the results of existing methods. In addition to implementing a new method for improving point clouds for orthophoto creation, the degree of distortion on the selected edge of four buildings has been greatly reduced when compared to the previous method. The success of the results with the latest proposed method of true orthophoto enhancement indicates an improvement of about 62% and 55% in the distortion decreasing of the structural edges and maintaining their coordinate accuracy. The proposed method did not uniformly reduce the distortions at the structural edges, and future advanced models could possibly improve it.  }, keywords = {orthophoto,Point Cloud,edge distortion}, title_fa = {روشی نوین در بهبود تضاریس لبه ساختمان‌ها بر روی تصاویر قائم}, abstract_fa = {امروزه تصاویر قائم از محصولات پرکاربرد در حوزه اطلاعات مکانی هستند که غالباً از تصاویر هوایی یا ماهواره ­ای تهیه ­می ­شوند به طوری که توجه به‌ دقت و کیفیت تصاویر قائم به دلیل دارا بودن هم ‌زمان اطلاعات هندسی و رادیومتریک از اهمیت بالایی برخوردار است. عوامل متعددی در کیفیت تهیه این تصاویر تاثیرگذار هستند که در این میان ابرنقاط و مدل رقومی سطحی که از آن تهیه می­ شوند را می­ توان به عنوان مهمترین موارد برشمرد. به سبب نقص ابرنقاط در لبه‌های ساختاری ساختمان­ ها تصاویر ­قائم حقیقی دارای اعوجاج‌ها و تضاریسی بر روی این لبه‌ها می­ باشند. این مشکل بر روی تصاویر قائم به‌دست‌آمده از تصاویری که با پهپادها در نواحی شهری اخذ می‌شوند به علت آنکه از ارتفاع پایین‌تری برخوردارند بیشتر است. در این حالت به سبب افزایش میزان جابجایی‌های مسطحاتی ناشی از عوارض مرتفع با ارتفاع پرواز پایین نسبت به هواپیماهای باسرنشین لازم است تا ابرنقاط مربوطه بهبود یافته و از مدل رقومی سطحی دقیق‌تری برای انجام تصحیحات استفاده شود. علاوه بر این روش‌های تهیه ابرنقاط که بر مبنای تناظریابی میان تصاویر است به علت وجود نواحی پنهان و تغییرات رادیومتریکی میان تصاویر همپوشان قادر به تولید ابرنقاط کامل نبوده و دارای نقص‌هایی به ‌ویژه بر روی لبه‌های عوارض هستند. در این مطالعه علاوه بر اینکه برای تکمیل ابرنقاط استفاده از شبکه یادگیری عمیق آموزش‌دیده در بهبود ابرنقاط برای تهیه تصاویر قائم پیشنهادشده است موفقیت نتایج حاصل از آن با جدیدترین روش پیشنهادی بهبود تصویر قائم حکایت از بهبود حدود 62 و 55 درصدی تضاریس نقاط واقع بر لبه‌های ساختاری و حفظ دقت مختصاتی آن‌ها دارد.}, keywords_fa = {تصویر قائم(ارتوفتو),ابرنقاط,تضاریس لبه}, url = {https://www.sepehr.org/article_699911.html}, eprint = {https://www.sepehr.org/article_699911_51e2f75a0768db8c174c46a07d8ccbd5.pdf} }