Araştırma Makalesi
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Building Extraction with K-Means and Fuzzy C-Means on Airborne Lidar Data

Yıl 2023, , 45 - 51, 31.12.2023
https://doi.org/10.51946/melid.1359718

Öz

Automatic building extraction plays a crucial role in many fields such as urban planning, disaster management, 3D building modeling, land evaluation, and updating GIS databases. Clustering is a data analysis method aimed at finding patterns and similar structures within the data. This method is often used to simplify information extraction in large datasets and holds significant importance, especially in fields like machine learning, data mining, and image analysis, serving as a fundamental tool in data analysis processes. Lidar is a remote sensing method that measures the distance from its location to the Earth's surface using pulsed laser and provides three-dimensional information about the shape and form of the Earth's surface. Lidar offers the advantage of obtaining three-dimensional data with less effort compared to traditional data sources. However, automatic building extraction from Lidar data remains a complex issue due to the inherent nature of the data. In this study, automatic building extraction from Lidar data was conducted using clustering-based approaches with the recommended methodology for point cloud processing and analysis. Specifically, the K-Means and Fuzzy C-Means clustering methods were applied to datasets containing different numbers of buildings. The results indicated that both K-Means and Fuzzy C-Means methods produced similar results. It was observed that the proximity, arrangement, and geometric structure of the point data played a significant role in the accuracy of the clustering methods.

Kaynakça

  • Adjiski V., Kaplan., G., & Mijalkovski, S. (2023). Assessment of the solar energy potential of rooftops using LiDAR datasets and GIS based approach. International Journal of Engineering and Geosciences, 8(2), 188-199.
  • Akbulut, Z., Özdemir, S., Acar, H., & Karsli, F. (2018). Automatic building extraction from image and LiDAR data with active contour segmentation. Journal of the Indian Society of Remote Sensing, 46, 2057-2068.
  • Alhawarat, M., & Hegazi, M. (2018). Revisiting k-means and topic modeling, a comparison study to cluster arabic documents. IEEE Access, 6, 42740-42749.
  • Awrangjeb, M., & Fraser, C. S. (2014). An automatic and threshold-free performance evaluation system for building extraction techniques from airborne LIDAR data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(10), 4184-4198.
  • Ben-Israel, A., & Iyigun, C. (2008). Probabilistic d-clustering. Journal of Classification, 25, 5-26.
  • Bezdek, J. C., Ehrlich, R., & Full, W. (1984). FCM: The fuzzy c-means clustering algorithm. Computers & geosciences, 10(2-3), 191-203.
  • Chen, D., Zhang, L., Li, J., & Liu, R. (2012). Urban building roof segmentation from airborne lidar point clouds. International journal of remote sensing, 33(20), 6497-6515.
  • Cheng, L., Zhao, W., Han, P., Zhang, W., Shan, J., Liu, Y., & Li, M. (2013). Building region derivation from LiDAR data using a reversed iterative mathematic morphological algorithm. Optics Communications, 286, 244-250.
  • Gilani, S. A. N., Awrangjeb, M., & Lu, G. (2016). An automatic building extraction and regularisation technique using lidar point cloud data and orthoimage. Remote Sensing, 8(3), 258.
  • Ghosh, S., & Dubey, S. K. (2013). Comparative analysis of k-means and fuzzy c-means algorithms. International Journal of Advanced Computer Science and Applications, 4(4).
  • Dhanachandra, N., Manglem, K., & Chanu, Y. J. (2015). Image segmentation using K-means clustering algorithm and subtractive clustering algorithm. Procedia Computer Science, 54, 764-771.
  • Du, S., Zhang, Y., Zou, Z., Xu, S., He, X., & Chen, S. (2017). Automatic building extraction from LiDAR data fusion of point and grid-based features. ISPRS journal of photogrammetry and remote sensing, 130, 294-307.
  • Kang, J., Min, L., Luan, Q., Li, X., & Liu, J. (2009). Novel modified fuzzy c-means algorithm with applications. Digital signal processing, 19(2), 309-319.
  • Kayi, A., Erdoğan, M., & Eker, O. (2015). Optech Ha-500 Ve Riegl Lms-Q1560 ile gerçekleştirilen LİDAR test sonuçları. Harita dergisi, 153(2), 42-46.
  • Lv, Z., Liu, T., Shi, C., Benediktsson, J. A., & Du, H. (2019). Novel land cover change detection method based on K-means clustering and adaptive majority voting using bitemporal remote sensing images. Ieee Access, 7, 34425-34437.
  • Meng, X., Wang, L., & Currit, N. (2009). Morphology-based building detection from airborne LIDAR data. Photogrammetric Engineering & Remote Sensing, 75(4), 437-442.
  • Miliaresis, G., & Kokkas, N. (2007). Segmentation and object-based classification for the extraction of the building class from LIDAR DEMs. Computers & geosciences, 33(8), 1076-1087.
  • Mongus, D., Lukač, N., & Žalik, B. (2014). Ground and building extraction from LiDAR data based on differential morphological profiles and locally fitted surfaces. ISPRS Journal of Photogrammetry and Remote Sensing, 93, 145-156.
  • Neeraj, K. N., & Maurya, V. (2020). A review on machine learning (feature selection, classification and clustering) approaches of big data mining in different area of research. Journal of Critical Reviews, 7(19), 2610-2626.
  • Ng, M. K. (2000). A note on constrained k-means algorithms. Pattern Recognition, 33(3), 515-519.
  • Ozdemir, E., Karsli, F., Kavzoglu, T., Bahadir, M., & Yagmahan, A. (2022). A novel algorithm for regularization of building footprints using raw LiDAR point clouds. Geocarto International, 37(25), 7358-7380.
  • Panda, S., Sahu, S., Jena, P., & Chattopadhyay, S. (2012). Comparing fuzzy-C means and K-means clustering techniques: a comprehensive study. In Advances in Computer Science, Engineering & Applications: Proceedings of the Second International Conference on Computer Science, Engineering and Applications (ICCSEA 2012), May 25-27, 2012, New Delhi, India, 1, 451-460.
  • Peker, N., & Kubat, C. (2021). Boyut Azaltmanın Bulanık C-Ortalama Kümeleme Teknikleri Üzerindeki Etkisi. Veri Bilimi, 4(1), 1-7.
  • Polat, N., & Uysal, M. (2016). Hava lazer tarama sistemi, uygulama alanları ve kullanılan yazılımlara genel bir bakış. Afyon Kocatepe Üniversitesi Fen ve Mühendislik Bilimleri Dergisi, 16(3), 679-692.
  • Sevgen, S. C. (2019). Airborne lidar data classification in complex urban area using random forest: a case study of Bergama, Turkey. International Journal of Engineering and Geosciences, 4(1), 45-51.
  • Sevgen, S. C., & Karsli, F. (2020). Automatic ground extraction for urban areas from airborne lidar data. Turkish Journal of Engineering, 4(3), 113-122.
  • Suganya, R., & Shanthi, R. (2012). Fuzzy c-means algorithm-a review. International Journal of Scientific and Research Publications, 2(11), 1.
  • Tarsha-Kurdi, F., Landes, T., & Grussenmeyer, P. (2008). Extended RANSAC algorithm for automatic detection of building roof planes from LiDAR data. The photogrammetric journal of Finland, 21(1), 97-109.
  • Verma, V., Kumar, R., & Hsu, S. (2006, Haziarn). 3D building detection and modeling from aerial LIDAR data. In 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06), 2, 2213-2220.
  • Yang, B., Xu, W., & Dong, Z. (2013). Automated extraction of building outlines from airborne laser scanning point clouds. IEEE Geoscience and Remote Sensing Letters, 10(6), 1399-1403.
  • Zeybek, M. (2022). Havasal LiDAR Nokta Bulutlarından Kentsel Yol Ağlarının Çıkarımı, Bergama Test Alanı. Türkiye Lidar Dergisi, 4(2), 53-59.

Hava Lidar Verisi Üzerinde K-Ortalamalar ve Bulanık C-Ortalama ile Bina Çıkarımı

Yıl 2023, , 45 - 51, 31.12.2023
https://doi.org/10.51946/melid.1359718

Öz

Otomatik bina çıkarımı, kentsel planlama, afet yönetimi, 3 boyutlu (3B) bina modelleme, arazi değerlemesi ve Coğrafi Bilgi Sistemleri (CBS) veri tabanlarının güncellenmesi gibi birçok alanda önemli bir rol oynamaktadır. Bu uygulamalarda, özellikle şehirlerin büyümesi ve gelişmesi ile birlikte bina yerleşimleri giderek karmaşık hale gelmektedir. Bu karmaşıklık, geleneksel yöntemlerle bu verilerin elde edilmesini ve güncellenmesini zorlaştırmaktadır. Kümeleme, veri içindeki desenleri ve benzer yapıları bulmayı amaçlayan bir veri analizi yöntemidir. Bu yöntem, genellikle büyük veri kümelerinde bilgi çıkarmayı basitleştirmek için kullanılır. Özellikle makine öğrenimi, veri madenciliği ve görüntü analizi gibi alanlarda, veri analizi süreçlerinde büyük bir öneme sahiptir. Veri analizi, verilerdeki önemli bilgileri çıkarmak ve bu bilgileri anlamak için temel bir araçtır. Lidar, darbeli lazer kullanarak kendi konumundan Dünya'nın yüzeyine olan mesafeyi ölçen ve Dünya'nın şekli ve formu hakkında üç boyutlu bilgi sunan bir uzaktan algılama yöntemidir. Hava Lidar verileri, özellik çıkarma, arazi modelleme ve Sayısal Yüzey Modeli oluşturma gibi uygulamalar için birçok araştırmacı tarafından kullanılmaktadır. Lidar, geleneksel veri toplama yöntemlerinden farklı olarak daha az iş gücü ile üç boyutlu veri oluşturma imkanı sağlar. Ancak Lidar verileri üzerinden otomatik bina çıkarımı, verinin doğası gereği karmaşık bir konudur. Bu çalışmada, Lidar verilerinden otomatik bina çıkarımı, nokta bulutu işleme ve analizi için önerilen yöntemlerle gerçekleştirilmiştir. Özellikle, K-Ortalamalar ve Bulanık C-Ortalamalar kümeleme yöntemleri, farklı bina sayıları içeren veri setlerine uygulanmıştır. Sonuçlar, K-Ortalamalar ve Bulanık C-Ortalamalar yöntemlerinin benzer sonuçlar ürettiğini göstermektedir. Nokta verilerinin yakınlığı, düzeni ve geometrik yapısı, kümeleme yöntemlerinin daha doğru sonuçlar üretmesinde önemli bir etken olduğu gözlenmiştir.

Kaynakça

  • Adjiski V., Kaplan., G., & Mijalkovski, S. (2023). Assessment of the solar energy potential of rooftops using LiDAR datasets and GIS based approach. International Journal of Engineering and Geosciences, 8(2), 188-199.
  • Akbulut, Z., Özdemir, S., Acar, H., & Karsli, F. (2018). Automatic building extraction from image and LiDAR data with active contour segmentation. Journal of the Indian Society of Remote Sensing, 46, 2057-2068.
  • Alhawarat, M., & Hegazi, M. (2018). Revisiting k-means and topic modeling, a comparison study to cluster arabic documents. IEEE Access, 6, 42740-42749.
  • Awrangjeb, M., & Fraser, C. S. (2014). An automatic and threshold-free performance evaluation system for building extraction techniques from airborne LIDAR data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(10), 4184-4198.
  • Ben-Israel, A., & Iyigun, C. (2008). Probabilistic d-clustering. Journal of Classification, 25, 5-26.
  • Bezdek, J. C., Ehrlich, R., & Full, W. (1984). FCM: The fuzzy c-means clustering algorithm. Computers & geosciences, 10(2-3), 191-203.
  • Chen, D., Zhang, L., Li, J., & Liu, R. (2012). Urban building roof segmentation from airborne lidar point clouds. International journal of remote sensing, 33(20), 6497-6515.
  • Cheng, L., Zhao, W., Han, P., Zhang, W., Shan, J., Liu, Y., & Li, M. (2013). Building region derivation from LiDAR data using a reversed iterative mathematic morphological algorithm. Optics Communications, 286, 244-250.
  • Gilani, S. A. N., Awrangjeb, M., & Lu, G. (2016). An automatic building extraction and regularisation technique using lidar point cloud data and orthoimage. Remote Sensing, 8(3), 258.
  • Ghosh, S., & Dubey, S. K. (2013). Comparative analysis of k-means and fuzzy c-means algorithms. International Journal of Advanced Computer Science and Applications, 4(4).
  • Dhanachandra, N., Manglem, K., & Chanu, Y. J. (2015). Image segmentation using K-means clustering algorithm and subtractive clustering algorithm. Procedia Computer Science, 54, 764-771.
  • Du, S., Zhang, Y., Zou, Z., Xu, S., He, X., & Chen, S. (2017). Automatic building extraction from LiDAR data fusion of point and grid-based features. ISPRS journal of photogrammetry and remote sensing, 130, 294-307.
  • Kang, J., Min, L., Luan, Q., Li, X., & Liu, J. (2009). Novel modified fuzzy c-means algorithm with applications. Digital signal processing, 19(2), 309-319.
  • Kayi, A., Erdoğan, M., & Eker, O. (2015). Optech Ha-500 Ve Riegl Lms-Q1560 ile gerçekleştirilen LİDAR test sonuçları. Harita dergisi, 153(2), 42-46.
  • Lv, Z., Liu, T., Shi, C., Benediktsson, J. A., & Du, H. (2019). Novel land cover change detection method based on K-means clustering and adaptive majority voting using bitemporal remote sensing images. Ieee Access, 7, 34425-34437.
  • Meng, X., Wang, L., & Currit, N. (2009). Morphology-based building detection from airborne LIDAR data. Photogrammetric Engineering & Remote Sensing, 75(4), 437-442.
  • Miliaresis, G., & Kokkas, N. (2007). Segmentation and object-based classification for the extraction of the building class from LIDAR DEMs. Computers & geosciences, 33(8), 1076-1087.
  • Mongus, D., Lukač, N., & Žalik, B. (2014). Ground and building extraction from LiDAR data based on differential morphological profiles and locally fitted surfaces. ISPRS Journal of Photogrammetry and Remote Sensing, 93, 145-156.
  • Neeraj, K. N., & Maurya, V. (2020). A review on machine learning (feature selection, classification and clustering) approaches of big data mining in different area of research. Journal of Critical Reviews, 7(19), 2610-2626.
  • Ng, M. K. (2000). A note on constrained k-means algorithms. Pattern Recognition, 33(3), 515-519.
  • Ozdemir, E., Karsli, F., Kavzoglu, T., Bahadir, M., & Yagmahan, A. (2022). A novel algorithm for regularization of building footprints using raw LiDAR point clouds. Geocarto International, 37(25), 7358-7380.
  • Panda, S., Sahu, S., Jena, P., & Chattopadhyay, S. (2012). Comparing fuzzy-C means and K-means clustering techniques: a comprehensive study. In Advances in Computer Science, Engineering & Applications: Proceedings of the Second International Conference on Computer Science, Engineering and Applications (ICCSEA 2012), May 25-27, 2012, New Delhi, India, 1, 451-460.
  • Peker, N., & Kubat, C. (2021). Boyut Azaltmanın Bulanık C-Ortalama Kümeleme Teknikleri Üzerindeki Etkisi. Veri Bilimi, 4(1), 1-7.
  • Polat, N., & Uysal, M. (2016). Hava lazer tarama sistemi, uygulama alanları ve kullanılan yazılımlara genel bir bakış. Afyon Kocatepe Üniversitesi Fen ve Mühendislik Bilimleri Dergisi, 16(3), 679-692.
  • Sevgen, S. C. (2019). Airborne lidar data classification in complex urban area using random forest: a case study of Bergama, Turkey. International Journal of Engineering and Geosciences, 4(1), 45-51.
  • Sevgen, S. C., & Karsli, F. (2020). Automatic ground extraction for urban areas from airborne lidar data. Turkish Journal of Engineering, 4(3), 113-122.
  • Suganya, R., & Shanthi, R. (2012). Fuzzy c-means algorithm-a review. International Journal of Scientific and Research Publications, 2(11), 1.
  • Tarsha-Kurdi, F., Landes, T., & Grussenmeyer, P. (2008). Extended RANSAC algorithm for automatic detection of building roof planes from LiDAR data. The photogrammetric journal of Finland, 21(1), 97-109.
  • Verma, V., Kumar, R., & Hsu, S. (2006, Haziarn). 3D building detection and modeling from aerial LIDAR data. In 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06), 2, 2213-2220.
  • Yang, B., Xu, W., & Dong, Z. (2013). Automated extraction of building outlines from airborne laser scanning point clouds. IEEE Geoscience and Remote Sensing Letters, 10(6), 1399-1403.
  • Zeybek, M. (2022). Havasal LiDAR Nokta Bulutlarından Kentsel Yol Ağlarının Çıkarımı, Bergama Test Alanı. Türkiye Lidar Dergisi, 4(2), 53-59.
Toplam 31 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Fotogrametri ve Uzaktan Algılama
Bölüm Araştırma Makaleleri
Yazarlar

Şeyma Akça 0000-0002-7888-5078

Erken Görünüm Tarihi 17 Aralık 2023
Yayımlanma Tarihi 31 Aralık 2023
Gönderilme Tarihi 13 Eylül 2023
Yayımlandığı Sayı Yıl 2023

Kaynak Göster

APA Akça, Ş. (2023). Hava Lidar Verisi Üzerinde K-Ortalamalar ve Bulanık C-Ortalama ile Bina Çıkarımı. Türkiye Lidar Dergisi, 5(2), 45-51. https://doi.org/10.51946/melid.1359718

Türkiye LiDAR Dergisi