شناسایی تغییرات ساختمان ها بر مبنای تئوری منطق فازی و مبتنی بر یادگیری عمیق با استفاده از مدل رقومی سطح و تصاویر ارتوفتو

نوع مقاله : مقاله پژوهشی

نویسندگان

1 کارشناسی ارشد دانشکده مهندسی نقشه برداری و اطلاعات مکانی، پردیس دانشکده های فنی دانشگاه تهران

2 دانشیاردانشکده مهندسی نقشه برداری و اطلاعات مکانی، پردیس دانشکده های فنی دانشگاه تهران

3 استادیار دانشکده مهندسی نقشه برداری و اطلاعات مکانی، پردیس دانشکده های فنی دانشگاه تهران

10.22131/sepehr.2022.699902

چکیده

امروزه به روزرسانی اطلاعات در مناطق شهری اهمیت بالایی دارد، زیرا این اطلاعات، اساس بسیاری از کاربردها را فراهم می­ کند که شامل مطالعات تغییرات پوشش اراضی و مطالعات محیطی است. روش ­های متعددی برای شناسایی تغییرات با به کارگیری داده ­های سنجش از دوری توسعه داده شده ­اند و روش ­­های جدیدی در حال ظهور هستند. در بسیاری از روش­ های شناسایی عوارض زمینی، این عوارض با استفاده از پیش ­دانسته ­هایی از جمله ساختار، بافت، خصوصیات بازتابی و غیره شناسایی می­ شوند. هدف از این تحقیق ارایه روشی برای شناسایی تغییرات ساختمان­ ها در دو  منطقه شهری و در بازه ­های زمانی 5 ساله و 3 ساله می­ باشد. در این تحقیق با توجه نوع داده ­های مورد استفاده و مناطق مورد مطالعه و تراکم ساختمان ­های شهری، روش شیءمبنا برای طبقه­ بندی عوارض و شناسایی ساختمان­ ها استفاده شده ­است. این روش شیءگرا، قطعه ­بندی چندمقیاسه است که با استفاده از آن توصیف گرهای مناسب طیفی، بافتی و ساختاری استخراج و با استفاده از روش ­های فازی، طبقه ­بندی می­ شوند و پس از طبقه ­بندی در دو اپک و استخراج ساختمان­ های حاصل از طبقه­ بندی، تغییرات ارتفاعی آنها محاسبه می­ شود. روش­ های شناسایی این تغییرات بر مبنای روش مبتنی بر یادگیری عمیق است و ارزیابی آن با استفاده از روش تفاضل DSM می ­باشد. در روش تفاضل  DSM با استفاده از یک حدآستانه ارتفاعی تغییرات شناسایی می ­شوند، سپس در روش مبتنی بر یادگیری عمیق با استفاده از یک شبکه عصبی کانولوشن بار دیگر با در اختیار داشتن مشخصه­ های ارتفاعی و داده­ های واقعیت ­زمین  ایجادشده از شناسایی تغییرات در حالت تفاضلی، این تغییرات ارتفاعی آشکار می­ شوند و با تغییرات شناسایی­ شده در روش تفاضلی ارزیابی می­ شوند. نتایج آزمون­ ها نشان داد با توجه نوع داده مورد استفاده، منطقه مورد مطالعه و تراکم ساختمان ­های موجود، حدود 96% ساختمان­ ها از تصاویر هوایی در گام اول شناسایی و استخراج شدند. همچنین در گام دوم شناسایی تغییرات ساختمانی به روش شبکه عصبی با صحت کلی 90% انجام شده است.

کلیدواژه‌ها


عنوان مقاله [English]

Using an Integration of Fuzzy Logic Theory and Deep Learning to Detect Changes in Buildings

نویسندگان [English]

  • Mohsen Abedi 1
  • Mohammad SaadatSeresht 2
  • Reza Shahhoseini 3
1 MSc in School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran
2 Associate Professor in School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran
3 Assistant Professor in School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran
چکیده [English]

Extended Abstract
Introduction
Nowadays, updating information collected from urban areas is of great importance, since it provides the basis for many fields of study such as land cover changes and environmental studies. Remote sensing provides an opportunity to obtain information from urban areas at different levels of accuracy while widely used in various change detection applications. Detecting changes in buildings as one of the most important features in urban areas is of particular importance. Powerful and expensive processing systems are the only way to process large volume of remote sensing and photogrammetry data generated by the ever increasing number of sources to which laymen do not have access. The present study has applied deep learning methods and high computational volume of data processing in free clouds to make this possible for the public.
 
Materials & Methods
Two case studies have been selected in the present study. The first includes DSM and Orthophoto images captured by drones from Mashhad in 2011 and 2016. DSM and Orthophoto images in the second case study has been collected by drones from Aqda in Yazd province in 2015 and 2018. In accordance with the type of data used and high computational volume used for processing, the present study has applied fuzzy clustering method to detect buildings with a high computational speed and deep learning method to detect their changes. Object-based method and fuzzy logic theory have been used in the first step to classify features and detect buildings. In the second step, deep learning method and DSM differentiation method were also used to detect changes in buildings and evaluate results obtained from deep learning method. In the first step, buildings were detected using descriptors extracted from terrestrial and non-terrestrial features, and related decisions were made using fuzzy logic. In the second step, DSM differentiation method has applied the masks extracted from buildings in both epochs on the related DSMs to find their difference and detects changes using an elevation threshold. In deep learning method, a convolutional neural network model was trained to detect changes in buildings during both epochs. Using the DSM of buildings in both epochs and a part of their interface, the network input layers were generated for training. Changes detected in the buildings by the differentiation method were also introduced as the output layer. Following the training and introducing the entire interface in both epochs as the input layer, the trained neural network has detect changes in the buildings. The same process was performed once more using the difference between two DSMs. In other words, a single input layer was used in the network and the rest of the process was the same as before. Finally, changes detected by the neural network was compared with changes detected in the DSM differentiation method
 
Results & Discussion
In the first step, buildings were detected and images were classified in accordance with the fuzzy logic. The overall accuracy of the first epoch classification in Mashhad equaled 94.6% indicating higher acuracy of object-based methods as compared to pixel-based methods. The overall accuracy of first epoch in Aqda equaled 95.5%. Neural network method detected changes in buildings with an overall accuracy of 90%. In accordance with the ground truth used in network training (both using DSMs as the input layer and the difference between the epochs as the input layer), results indicated that deep learning method is highly accurate in one-dimensional convolution mode. Moreover, the second step has applied the difference between DSMs in the two epochs and thus, many areas lacking a change in height were removed in both epochs and the network was trained more appropriately and accurately.
 
Conclusion          
Necessity of extracting features, especially urban features such as buildings and identifying their changes over time have been investigated in the present study. Due to the high computational volume of modern remote sensing and photogrammetry data and highly expensive systems required for their processing, a new method was presented in the present study to solve this problem. Considering the type of data used and the complexity of features, object-based methods were selected instead of pixel-based methods to identify features and buildings. Deep learning method was used to detect changes in buildings. The method was also compared with DSM differentiation method. A one-dimensional convolutional neural network was used in the deep learning method. Two different modes were used in the network to train and predict changes. In the first, DSMs extracted from the buildings in each epoch were used as the input layer, while in the second one, the difference between DSMs were introduced as a single input layer to the network and the network was trained in accordance with the ground truth collected from areas with and without change obtained from the DSM differentiation method. Following the training process, changes were predicted using the trained network. Much better results were obtained from the second mode in which the difference between DSMs were used.
 

کلیدواژه‌ها [English]

  • Multiresolution Segmentation
  • Fuzzy Clustering
  • Deep Learning
  • Convolution neural network
1- رشیدی،پ؛راستی،ح، 1396،شناسایی تغییرات ساختمان‌ ها با استفاده از داده های لیزراسکنرهوایی،همایش ملی ژئوماتیک، 24
2- Awrangjeb, M., Fraser, C. S., & Lu, G. (2015). BUILDING CHANGE DETECTION FROM LIDAR POINT CLOUD DATA BASED ON CONNECTED COMPONENT ANALYSIS. ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, II-3/W5(3W5), 393–400. https://doi.org/10.5194/isprsannals-II-3-W5-393-2015
2- Bezdek, J. C., Ehrlich, R., & Full, W. (1984). FCM: The fuzzy c-means clustering algorithm. Computers & Geosciences, 10(2–3), 191–203. https://doi.org/10.1016/0098-3004(84)90020-7
3- Blaschke, T., & Hay, G. J. (2001). Object-oriented image analysis and scale-space: theory and methods for modeling and evaluating multiscale landscape structure. International Archives of Photogrammetry and Remote Sensing, 34(4), 22–29.
4- Duda, R. O., & Hart, P. E. (1973). Pattern Classification and Scene Analysis. Wiley. Retrieved from https://books.google.com.br/books?id=POMGRAAACAAJ
5- Dunn, J. C. (1973). A Fuzzy Relative of the ISODATA Process and Its Use in Detecting Compact Well-Separated Clusters. Journal of Cybernetics, 3(3), 32–57. https://doi.org/10.1080/01969727308546046
6- hajahmadi, S. (2013). Using satellite imagery and digital maps to change detection in urban areas. K. N. Toosi University of Technology.
7- LeCun, Y., Bottou, L., Bengio, Y., &Haffner, P. (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11), 2278–2323. https://doi.org/10.1109/5.726791
8- Lim, H., Park, J., Lee, K., & Han, Y. (2018). RARE SOUND EVENT DETECTION USING 1D CONVOLUTIONAL RECURRENT NEURAL NETWORKS.
9- MacLean, M., &Congalton, R. (2012). Map accuracy assessment issues when using an object-oriented approach. American Society for Photogrammetry and Remote Sensing Annual Conference 2012, ASPRS 2012, 369–373.
10- Meng, X., Currit, N., Wang, L., & Yang, X. (2012). Detect Residential Buildings from Lidar and Aerial Photographs through Object-Oriented Land-Use Classification. Photogrammetric Engineering & Remote Sensing, 78, 35–44. https://doi.org/10.14358/PERS.78.1.35
11- Mohammadzade, A., Varesi, A., &Janalipour, M. (2017). Presentation of a Method for Detecting Urban Growth using Spectral- Spatial Variation Indicators and Remote Sensing Data. Journal of Geomatics Science and Technology, 6(4). Retrieved from http://jgst.issge.ir/article-1-574-fa.html
12- Nahhas, F. H., Shafri, H. Z. M., Sameen, M. I., Pradhan, B., &Mansor, S. (2018). Deep Learning Approach for Building Detection Using LiDAR–Orthophoto Fusion. Journal of Sensors, 2018, 1–12. https://doi.org/10.1155/2018/7212307
13- Ojaghi, S., Ebadi, H., Ahmadi, F., &Teymoori, M. (2015). high resolution images classification with object based method.
14- sahebi,  mahmoodreza. (2015). change detection in semi urban areas with optical temporal Satellite Images Based on Object based Analysis and svm. K. N. Toosi University of Technology.
15- Sameen, M. I., & Pradhan, B. (2017). A Two-Stage Optimization Strategy for Fuzzy Object-Based Analysis Using Airborne LiDAR and High-Resolution Orthophotos for Urban Road Extraction. Journal of Sensors, 2017, 1–17. https://doi.org/10.1155/2017/6431519
16- SINGH, A. (1989). Review Article Digital change detection techniques using remotely-sensed data. International Journal of Remote Sensing, 10(6), 989–1003. https://doi.org/10.1080/01431168908903939
17- Vakalopoulou, M., Karantzalos, K., Komodakis, N., &Paragios, N. (2015). Building detection in very high resolution multispectral data with deep learning features. In 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) (Vol. 2015–Novem, pp. 1873–1876). IEEE. https://doi.org/10.1109/IGARSS.2015.7326158
18- Walter, V. (2004). Object-based classification of remote sensing data for change detection. ISPRS Journal of Photogrammetry and Remote Sensing, 58(3–4), 225–238. https://doi.org/10.1016/j.isprsjprs.2003.09.007
19- Zhang, W., Qi, J., Wan, P., Wang, H., Xie, D., Wang, X., & Yan, G. (2016). An easy-to-use airborne LiDAR data filtering method based on cloth simulation. Remote Sensing, 8(6), 501. https://doi.org/10.3390/rs8060501
20- Zong, K., Sowmya, A., &Trinder, J. (2013). Kernel partial least squares based hierarchical building change detection using high resolution aerial images and lidar data. In 2013 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2013 (pp. 1–7). IEEE. https://doi.org/10.1109/DICTA.2013.6691502