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Uzaktan Algılanmış Görüntülerin SURF Özellik Verileri ve RANSAC Algoritması İle Otomatik Çakıştırılması

Year 2019, Volume: 9 Issue: 3, 425 - 432, 15.07.2019
https://doi.org/10.17714/gumusfenbil.486585

Abstract

Günümüzde
algılayıcı çeşitliliği ile birlikte artan veri yoğunluğu sebebiyle, uzaktan
algılama ve fotogrametrik değerlendirme süreçlerinde çakıştırma aşamasının
otomatizasyonuna yönelik araştırmalar yoğunlaşmıştır. Otomatik çakıştırma
algoritmalarından; çevresel izleme, değişim analizi, sınıflandırma, görüntü
kaynaştırma gibi birçok çalışmada faydalanılmaktadır. Bu çalışmada farklı
zamanlarda değişik sensörlerce (alıcılarla) kaydedilen uzaktan algılanmış çok
bantlı görüntülerin otomatik çakıştırılmasına yönelik özellik tabanlı bir
yaklaşım önerilmiştir. Bu yaklaşımın özellik çıkarma aşamasında SURF (Speeded-up
Robust Feature) algoritmasının TBA (Temel Bileşen Analizi) yardımıyla
iyileştirilmiş çok bantlı veri setlerinin ilk bandına uygulanması yoluyla
verinin tüm bantlarındaki spektral bilgiden optimum düzeyde faydalanılmıştır. Bu
aşamada belirlenen ilgi noktalarına ilişkin 64 boyutlu özellik vektörleri
yardımıyla hesaplanan KFT(Karesel Farklar Toplamı) değerleri kritize edilerek
eşlenik noktalar tespit edilmiştir. Ardından eşlenik noktalar arasında zayıf
olanlar RANSAC (Random Sample Consensus) yardımıyla elemine edilerek kalan
noktalar ile projektif dönüşüm modeli için homografi tanımlanmıştır. Son
aşamada hesaplanan homografi matrisi kullanılarak geometrik dönüşüm uygulanan
görüntüler yeniden örnekleme sonrasında jeoreferanslı olarak kaydedilmiştir.
Önerilen yaklaşımın testi için 2003, 2008 ve 2015 tarihlerinde farklı
sensörlerce algılanmış çok bantlı dijital hava görüntülerinden faydalanılmıştır.
Bunlardan 2015 tarihli görüntü ortofoto olup referans görüntüsü olarak
kullanılmıştır. Çalışmada önerilen yaklaşımın uygulanması sonucunda; 2003
görüntüsü için ± 0.61m, 2008 görüntüsü için ise ± 0.53m KOH (Karesel Ortalama
Hata) düzeyinde konumsal doğruluk elde edilmiştir.

References

  • Acar, H., Karsli, F., Dihkan, M. (2017). Automatic 3D Coordinate Extraction from High Resolution Digital Aerial Images. Journal of the Indian Society of Remote Sensing, 45(2), 209-216.
  • Bay, H., Ess, A., Tuytelaars, T., Van Gool, L. (2008). Speeded-up robust features (SURF). Computer vision and image understanding, 110(3), 346-359.
  • Brown, L. G. (1992). A survey of image registration techniques. ACM computing surveys (CSUR), 24(4), 325-376.
  • Brown, M., Lowe, D. G. (2002). Invariant features from interest point groups. In: BMVC, 4.
  • Fischler, M. A., Bolles, R. C. (1981). Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Communications of the ACM, 24(6), 381-395.
  • Fonseca, L. M., & Manjunath, B. S. (1996). Registration techniques for multisensor remotely sensed imagery. PE & RS- Photogrammetric Engineering & Remote Sensing, 62(9), 1049-1056.
  • Gonçalves, H., Gonçalves, J. A., Corte-Real, L. (2011a). HAIRIS: A method for automatic image registration through histogram-based image segmentation. IEEE transactions on image processing, 20(3), 776-789.
  • Gonçalves, H., Corte-Real, L., Gonçalves, J. A. (2011b). Automatic image registration through image segmentation and SIFT. IEEE Transactions on Geoscience and Remote Sensing, 49(7), 2589-2600.
  • Hartley, R., Zisserman, A. (2003). Multiple view geometry in computer vision. Cambridge university press.
  • Harris, C., Stephens, M. (1988). A combined corner and edge detector. In: Alvey vision conference, 15(50), 10-5244.
  • Li, Q., Wang, G., Liu, J., Chen, S. (2009). Robust scale-invariant feature matching for remote sensing image registration. IEEE Geoscience and Remote Sensing Letters, 6(2), 287-291.
  • Lowe, D. G. (2004). Distinctive image features from scale-invariant keypoints. International journal of computer vision, 60(2), 91-110.
  • Ma, W., Wen, Z., Wu, Y., Jiao, L., Gong, M., Zheng, Y., Liu, L. (2017). Remote sensing image registration with modified SIFT and enhanced feature matching. IEEE Geoscience and Remote Sensing Letters, 14(1), 3-7.
  • Rosten, E., Drummond, T. (2006). Machine learning for high-speed corner detection. In: European conference on computer vision, 430-443, Springer, Berlin, Heidelberg.
  • Smith, S. M., Brady, J. M. (1997). SUSAN - a new approach to low level image processing. International journal of computer vision, 23(1), 45-78.
  • Yang, K., Pan, A., Yang, Y., Zhang, S., Ong, S. H., Tang, H. (2017). Remote sensing image registration using multiple image features. Remote Sensing, 9(6), 581.
  • Zitova, B., Flusser, J. (2003). Image registration methods: a survey. Image and vision computing, 21(11), 977- 1000.

Automatic Registration of Remotely Sensed Images by Using SURF Features and RANSAC Algorithm

Year 2019, Volume: 9 Issue: 3, 425 - 432, 15.07.2019
https://doi.org/10.17714/gumusfenbil.486585

Abstract

Intensive work has been carried out for
optimization of automatic registration using remotely sensed data and
photogrammetric techniques because of very large and various datasets
availability. Automatic registration is used in many remote sensing mapping
applications such as environmental monitoring, change detection,
classification, image fusion, etc. In this study, a feature based approach was
proposed for automatic registration which can be used for automatic
registration of multispectral images acquired in different periods. This
technique suggests an optimization of multiband spectral data generated by PCA (Principal
Component Analysis) transformation. The multispectral image data was first
evaluated using PCA then the SURF (Speeded up Robust Feature) algorithm was
applied on the optimized first band of the processed image to detect interest
points. In order to decide on matching points used SSD (Sum of Square
Distances) values are calculated using interest points data with 64 dimensional
feature vectors. As a step forward weak points were eliminated by applying
RANSAC (Random Sample Consensus) method and the remaining point data were used
for determining homography which is necessary for projective transformation. In
the last step, georeferenced images that were geometrically transformed using
homography matrix were saved after resampling process. In order to test the
proposed approach multispectral aerial images from 2003, 2008 and 2015 were
used. The orthophoto image of 2015 was used as reference data. As a result
spatial accuracies were found with RMSE values as ± 0.61m and ± 0.53m for the
years 2003 and 2008 respectively.

References

  • Acar, H., Karsli, F., Dihkan, M. (2017). Automatic 3D Coordinate Extraction from High Resolution Digital Aerial Images. Journal of the Indian Society of Remote Sensing, 45(2), 209-216.
  • Bay, H., Ess, A., Tuytelaars, T., Van Gool, L. (2008). Speeded-up robust features (SURF). Computer vision and image understanding, 110(3), 346-359.
  • Brown, L. G. (1992). A survey of image registration techniques. ACM computing surveys (CSUR), 24(4), 325-376.
  • Brown, M., Lowe, D. G. (2002). Invariant features from interest point groups. In: BMVC, 4.
  • Fischler, M. A., Bolles, R. C. (1981). Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Communications of the ACM, 24(6), 381-395.
  • Fonseca, L. M., & Manjunath, B. S. (1996). Registration techniques for multisensor remotely sensed imagery. PE & RS- Photogrammetric Engineering & Remote Sensing, 62(9), 1049-1056.
  • Gonçalves, H., Gonçalves, J. A., Corte-Real, L. (2011a). HAIRIS: A method for automatic image registration through histogram-based image segmentation. IEEE transactions on image processing, 20(3), 776-789.
  • Gonçalves, H., Corte-Real, L., Gonçalves, J. A. (2011b). Automatic image registration through image segmentation and SIFT. IEEE Transactions on Geoscience and Remote Sensing, 49(7), 2589-2600.
  • Hartley, R., Zisserman, A. (2003). Multiple view geometry in computer vision. Cambridge university press.
  • Harris, C., Stephens, M. (1988). A combined corner and edge detector. In: Alvey vision conference, 15(50), 10-5244.
  • Li, Q., Wang, G., Liu, J., Chen, S. (2009). Robust scale-invariant feature matching for remote sensing image registration. IEEE Geoscience and Remote Sensing Letters, 6(2), 287-291.
  • Lowe, D. G. (2004). Distinctive image features from scale-invariant keypoints. International journal of computer vision, 60(2), 91-110.
  • Ma, W., Wen, Z., Wu, Y., Jiao, L., Gong, M., Zheng, Y., Liu, L. (2017). Remote sensing image registration with modified SIFT and enhanced feature matching. IEEE Geoscience and Remote Sensing Letters, 14(1), 3-7.
  • Rosten, E., Drummond, T. (2006). Machine learning for high-speed corner detection. In: European conference on computer vision, 430-443, Springer, Berlin, Heidelberg.
  • Smith, S. M., Brady, J. M. (1997). SUSAN - a new approach to low level image processing. International journal of computer vision, 23(1), 45-78.
  • Yang, K., Pan, A., Yang, Y., Zhang, S., Ong, S. H., Tang, H. (2017). Remote sensing image registration using multiple image features. Remote Sensing, 9(6), 581.
  • Zitova, B., Flusser, J. (2003). Image registration methods: a survey. Image and vision computing, 21(11), 977- 1000.
There are 17 citations in total.

Details

Primary Language Turkish
Subjects Engineering
Journal Section Articles
Authors

Mustafa Dihkan 0000-0002-0027-236X

Publication Date July 15, 2019
Submission Date November 22, 2018
Acceptance Date February 11, 2019
Published in Issue Year 2019 Volume: 9 Issue: 3

Cite

APA Dihkan, M. (2019). Uzaktan Algılanmış Görüntülerin SURF Özellik Verileri ve RANSAC Algoritması İle Otomatik Çakıştırılması. Gümüşhane Üniversitesi Fen Bilimleri Dergisi, 9(3), 425-432. https://doi.org/10.17714/gumusfenbil.486585