JOURNAL BROWSE
Search
Advanced SearchSearch Tips
Evaluation on Tie Point Extraction Methods of WorldView-2 Stereo Images to Analyze Height Information of Buildings
facebook(new window)  Pirnt(new window) E-mail(new window) Excel Download
 Title & Authors
Evaluation on Tie Point Extraction Methods of WorldView-2 Stereo Images to Analyze Height Information of Buildings
Yeji, Kim; Yongil, Kim;
  PDF(new window)
 Abstract
Interest points are generally located at the pixels where height changes occur. So, interest points can be the significant pixels for DSM generation, and these have the important role to generate accurate and reliable matching results. Manual operation is widely used to extract the interest points and to match stereo satellite images using these for generating height information, but it causes economic and time consuming problems. Thus, a tie point extraction method using Harris-affine technique and SIFT(Scale Invariant Feature Transform) descriptors was suggested to analyze height information of buildings in this study. Interest points on buildings were extracted by Harris-affine technique, and tie points were collected efficiently by SIFT descriptors, which is invariant for scale. Searching window for each interest points was used, and direction of tie points pairs were considered for more efficient tie point extraction method. Tie point pairs estimated by proposed method was used to analyze height information of buildings. The result had RMSE values less than 2m comparing to the height information estimated by manual method.
 Keywords
Stereo Image;Tie Point;Interest Point;Height Information;Urban;Building;
 Language
Korean
 Cited by
 References
1.
Cheng, F. and Thiel, K. H. (1995), Delimiting the building heights in a city from the shadow in a panchromatic SPOT-image—Part 1 Test of forty-two buildings, Remote Sensing, Vol. 16, No. 3, pp. 409-415. crossref(new window)

2.
Cheng, L., Tong, L., Chen, Y., Zhang, W., Shan, J., Liu, Y., and Li, M. (2013), Integration of LiDAR data and optical multiview images for 3D reconstruction of building roofs, Optics and Lasers in Engineering, Vol. 51, No. 4, pp. 493-502. crossref(new window)

3.
Han, D., Kim, D., Lee, J., Oh, J., and Kim, Y. (2006), Automatic image-to-image registration of middle- and low-resolution satellite image using scale-invariant feature transform technique, Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography, Vol. 24, No. 5, pp. 409-416. (in Korean with English abstract)

4.
Harris, C. and Stephens, M. (1988), A combined corner and edge detector, In: Fourth Alvey Vision Conference, Manchester, UK, pp. 147-151.

5.
Izadi, M. and Saeedi, P. (2012), Three-dimensional polygonal building model estimation from single satellite images, IEEE Transactions on Geoscience and Remote Sensing, Vol. 50, No. 6, pp. 2254-2272. crossref(new window)

6.
Kim, E. M., Sohn, H. G., and Song, Y. S. (2005), Comparison of interest point operators for image matching, Proceedings of the Korean Society of Civil Engineers, Vol. 25, No. 4D, pp. 591-597. (in Korean with English abstract)

7.
Kim, H. J., Han, D. Y., and Kim, Y. I. (2006), Building height extraction using triangular vector structure from a single high resolution satellite image, Journal of the Korean Society of Remote Sensing, Vol. 22, No. 6, pp. 621-626.

8.
Kim, Y., Han, Y., Yeom, J, and Kim, Y. (2013), Comparative study of interest point extraction algorithms for image matching using high resolution satellite images on urban objects, Proceeding of 2013 International Symposium of Remote Sensing. pp. 833-836.

9.
Lee, T. Y., Kim, Y. S., and Kim, T. J. (2013), Extraction of 3D building information by modified volumetric shadow analysis using high resolution panchromatic and multispectral images, Korean Journal of Remote Sensing, Vol. 29, No. 5, pp. 499-508. (in Korean with English abstract) crossref(new window)

10.
Lowe, D. G. (2004), Distinctive image features from scaleinvariant keypoints, International Journal of Computer Vision, Vol. 2, No. 60, pp. 91-110.

11.
Mikolajcyk, K. and Schmid, C. (2002), An affine invariant interest point detector, International Conference on Computer Vision, Vol. 2350, pp. 128-142.

12.
Mikolajczyk, K. and Schmid, C. (2004), Scale & affine invariant interest point detectors, International Journal of Computer Vision, Vol. 60, No. 1, pp. 63-86. crossref(new window)

13.
Sportouche, H., Tupin, F., and Denise, L. (2009), Building extraction and 3D reconstruction in urban areas from high-resolution optical and SAR imagery, Urban Remote Sensing Event, 2009 Joint. IEEE, pp. 1-11.

14.
Verma, V., Kumar, R., and Hsu, S. (2006), 3D building detection and modeling from aerial LIDAR data, IEEE Computer Society Conference on. Computer Vision and Pattern Recognition, 2006, Vol. 2. pp. 2213-2220.

15.
Xu, F. and Jin, Y.. (2007), Automatic reconstruction of building objects from multiaspect meter-resolution SAR images, IEEE Transactions on Geoscience and Remote Sensing, Vol. 45, No. 7, pp. 2336-2353. crossref(new window)

16.
Yu, B., Liu, H., Wu, J., Hu, Y., and Zhang, L. (2010), Automated derivation of urban building density information using airborne LiDAR data and object-based method, Landscape and Urban Planning, Vol. 98, No. 3, pp. 210-219. crossref(new window)

17.
Zhang, L. and Gruen, A. (2006), Multi-image matching for DSM generation from IKONOS imagery, ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 60, No. 3, pp. 195-211. crossref(new window)