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GÖRÜNTÜ VE LIDAR VERİSİNDEN BİNA TESPİTİNDE FARKLI YÖNTEMLER

VARIOUS METHODS TO DETECT BUILDINGS USING IMAGE AND LIDAR DATA

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Abstract (2. Language): 
Four different variants of building detection are presented. Each variant has a different workflow and is capable of detecting buildings. The first variant of building detection is based on multispectral classification and DSM filtering. In the second variant, DSM blobs, mainly consisting of buildings and trees, are detected by subtraction of the DTM from the DSM. Then, trees are eliminated using NDVI data, derived from unsupervised ISODATA classification of the multispectral images, while small non-building objects are rejected based on size criteria. The third variant uses the planimetric density of raw LIDAR DTM data to detect the above-ground objects. The fourth variant is like the third one, but uses the vertical density of the raw LIDAR data (all points) to distinguish trees and buildings. To improve the results, a combination of the four variants using set intersections and unions is performed. The combination was empirical, with consideration of the datasets used in each variant and the advantages and disadvantages of each variant. In the evaluation, the combination of the four individual results yields 94% correct detections and an omission error of 12% for Zurich airport dataset.
Abstract (Original Language): 
Bu çalışmada dört farklı bina tespit yöntemi incelenmiştir. Her bir yöntem farklı işlem adımlarına sahiptir ve bina tespiti için farklı üstünlüklere sahiptir. İlk yöntem, çok bantlı sınıflandırma ve sayısal yükseklik modeli filtrelenmesine dayanmaktadır. İkinci yöntem, sayısal arazi modeli üzerinde tespit edilmiş arazi üzeri nesneleri ile NDVI görüntüsünün eğitimsiz sınıflandırması ile ağaç nesnelerinin elemine edilmesi ile binaları tespit etmektedir. Üçüncü yöntem, LIDAR arazi noktaları üzerinde yoğunluk analizi ile arazi üzeri nesnelerin tespiti ve yine NDVI kullanarak binaları tespit etmektedir. Dördüncü yöntem ise tamamen LIDAR noktalarına dayanmaktadır, dikey ve yatay düzlem üzerinde yoğunluk analizi ile bina ve ağaçların birbirlerinden ayrışmasını inceleyerek binalar tespit edilmiştir. Daha sonra, yöntemlerin özelliklerine, avantaj ve dezavantajlarına bağlı olarak sonuçlar bütünleştirilmiş ve sırasıyla %94 ve %92 doğruluk ve tamlık değerlerine ulaşılmıştır.
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REFERENCES

References: 

[1] Brenner, C., (2000) "Towards fully automatic
generation of city models" International Archives of
the Photogrammetry, Remote Sensing and Spatial
Information Sciences, 33-B3/1, 85-92.
[2] Brenner,C., (2005) "Building reconstruction
from images and laser scannin" International Journal
of Applied Earth Observation and Geoinformation, 6-
3/4, 187-198.
[3] Haala, N., Kada, M., (2010) "An update on
automatic 3D building reconstruction" ISPRS Journal
of Photogrammetry and Remote Sensing, 65-6,570-
580.
[4] Lin, C., Huertas, A., Nevatia, R., (1995)
"Detection of buildings from monocular images"
Automatic Extraction of Man-Made Objects from
Aerial and Space Images, pp. 125-134, Eds. Gruen,
A., Kuebler, O., P. Agouris, P. , Birkhäuser Verlag,
Basel.
[5] Katartzis, A., Sahli, H., Pizurica, V., Cornelis,
J., (2001) "A Model-based approach to the automatic
extraction of linear features from airborne images"
IEEE Transactions on Geoscience and Remote
Sensing, 39-9, 2073 -2079.
[6] Henricsson, O., E. Baltsavias, E., (1997) "3-D
building reconstruction with ARUBA: a qualitative
and quantitative evaluation" Automatic Extraction of
Man-Made Objects from Aerial and Space Images
(II), pp. 65-76, Eds. A. Gruen, A., O. Henricsson, O.,
Birkhauser Verlag. Basel.
[7] Baillard, C., Zisserman, A., (1999) "Automatic
reconstruction of piecewise planar models from
multiple views" Proc. of the IEEE Computer Society
Görüntü ve LIDAR Verisinden Bina Tespitinde Farklı Yöntemler
DEMİR
65
Conference on Computer Vision and Pattern
Recognition, Colorado, USA, June 23-25, 559-565.
[8] Baltsavias, E.P., (1999) "A comparison
between photogrammetry and laser scanning" ISPRS
Journal of Photogrammetry and Remote Sensing, 54-
2/3, 83-94.
[9] Rottensteiner, F., Trinder, J., Clode, S., Kubik,
K., (2005) "Automated delineation of roof planes
from LIDAR data" International Archives of the
Photogrammetry, Remote Sensing and Spatial
Information Sciences, 36-3/W19, 221-226.
[10] Sohn, G., Dowman, I.J., (2007) " Data fusion
of high-resolution satellite imagery and LiDAR data
for automatic building extraction" ISPRS Journal of
Photogrammetry and Remote Sensing. 62-1, 43-63.
[11] Kim, C., Habib, A., (2009) "Object-based
integration of photogrammetric and LiDAR data for
automated generation of complex polyhedral building
models" Sensors, 9-7, 5679-5701.
[12] Demir, N., (2013) "Combination of Airborne
Laser and Image Data for Building Detection and 3D
Roof Reconstruction" Doktora Tezi, ETH Zurich.
[13] Richards, J.A., (1993) "Remote sensing digital
image analysis" Springer, New York.
[14] Schnabel, R., Wahl, R., Klein, R (2007)
"Efficient RANSAC for point-cloud shape detection"
Proc.of Computer Graphics Forum, 214-226.

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