Öz
Kaynakça
- 1. Akar, Ö., (2013). Rastgele Orman Sınıflandırıcısına Doku Özellikleri Entegre Edilerek Benzer Spektral Özellikteki Tarımsal Ürünlerin Sınıflandırılması. Doktora Tezi, Karadeniz Teknik Üniversitesi, Fen Bilimleri Enstitüsü, Trabzon.
- 2. Özkan, Y., (2008). Veri Madenciliği Yöntemleri, Papatya Yayıncılık, İstanbul.
- 3. https://www.harrisgeospatial.com/docs/SupportVectorMachine.html Access Date: 30.10.2017.
- 4. Vapnik, V.N., (2000). The Nature of Statistical Learning Theory, Springer-Verlag, New York.
- 5. Wang, L., (2005). Support Vector Machines: Theory and Applications. Springer-Verlag, Berlin, Heidelberg.
- 6. Key, J., Maslanik, J.A., and Schweiger, A.J., (1989). Classification of Merged AVHRR and SMMR Arctic Data with Neural Networks. Photogrammetric Engineering and Remote Sensing, 55, 9, 1331-1338.
- 7. Benediktsson, J.A., Swain, P.H., and Ersoy, O.K., (1990). Neural Network Approaches Versus Statistical Methods in Classification of Multisource Remote Sensing Data. IEEE Transactions on Geoscience and Remote Sensing, 28, 4, 540-552.
- 8. Lee, J., Weger, R.C., Sengupta, S.K., and Welch, R.M., (1990). A Neural Network Approach to Cloud Classification. IEEE Transactions on Geoscience and Remote Sensing, 28, 5, 846-855.
- 9. Tso, A. and Mather, P.M., (2009). Classification Methods for Remotely Sensed Data.
- 10. Duda, R.O., Peter, E.H., and Stork, D.G., (2012). Pattern Classification, John Wiley & Sons.
- 11. Schowengerdt, R.A., (2007). Remote Sensing: Models and Methods for Image Processing, Third Edition.
- 12. Kanellopoulos, I. and Wilkinson, G.G., (1997). Strategies and Best Practice for Neural Network Image Classification. International Journal of Remote Sensing, 18, 711-725.
- 13. Foody, G.M., (1995). Land Cover Classification by an Artificial Neural Network with Ancillary Information. International Journal of Geographical Information Systems, 9, 5, 527-542.
- 14. Atkinson, P.M. and Tatnall, A.R.L., (1997). Introduction Neural networks in remote sensing. International Journal of Remote Sensing, 18, 4, 699-709.
- 15. Rumelhart, D.E., Hinton, G.E. and Williams, R.J., (1986). Learning Internal Representations by Error Propagation, Parallel Distributed Processing: Explorations in the Microstruction of Cognition. Rumelhart D.E. and McClelland J.L. (Eds.), Cambridge, MA, The MIT Press. I, 318-362.
- 16. ENVI Field Guide.
- 17. Hsu, C.W., Chang, C.C., and Lin, C.J., (2008). A Practical Guide to Support Vector Classification.
- 18. Congalton, R.G. and Green, K., (1999). Assessing the Accuracy of Remotely Sensed Data: Principles and Practices, Boca Raton, FL: Lewis.
- 19. Yilmaz, V., Konakoglu, B., Serifoglu, C., Gungor, O., and Gökalp, E., (2016). Image Classification-Based Ground Filtering of Point Clouds Extracted from UAV-based Aerial Photos. Geocarto International, 1-11.
- 20. Akar, A., Gökalp, E., Akar, Ö., and Yılmaz, V., (2017). Improving Classification Accuracy of Spectrally Similar Land Covers in the Rangeland and Plateau Areas with a Combination of WorldView-2 and UAV images. Geocarto International, 32, 9, 990-1003.
- 21. http://spatial-analyst.net/ILWIS/htm/ilwismen/confusion_matrix.htm, Access Date: 29.10.2017.
Öz
Remote sensing technologies
are used in many applications to extract information from the surface of the
earth. Image classification, which is one of the most widely-used ways of
information extraction, is a controversial topic in remote sensing. This is because
all classification algorithms introduced in the literature cause classification
errors to some extent. Simple classification algorithms like Minimum Distance,
Parallelpiped and Mahalanobis Distance commit a large amount of classification
errors. This, of course, has encouraged the remote sensing community to develop
more advanced classification algorithms to further increase classification
accuracy. This study uses sophisticated classification algorithms Support
Vector Machines (SVM), k-Nearest Neighbour (kNN) and Artificial Neural Network
(ANN) to classify a WorldView-2 multispectral image in order to produce land
cover maps. The accuracies of the produced thematic maps were evaluated with
randomly-selected control points. The SVM algorithm classified the imagery with
the best classification accuracy of 72.38%.
Anahtar Kelimeler
Image Classification Support Vector Machines k-Nearest Neighbour Artificial Neural Network
Kaynakça
- 1. Akar, Ö., (2013). Rastgele Orman Sınıflandırıcısına Doku Özellikleri Entegre Edilerek Benzer Spektral Özellikteki Tarımsal Ürünlerin Sınıflandırılması. Doktora Tezi, Karadeniz Teknik Üniversitesi, Fen Bilimleri Enstitüsü, Trabzon.
- 2. Özkan, Y., (2008). Veri Madenciliği Yöntemleri, Papatya Yayıncılık, İstanbul.
- 3. https://www.harrisgeospatial.com/docs/SupportVectorMachine.html Access Date: 30.10.2017.
- 4. Vapnik, V.N., (2000). The Nature of Statistical Learning Theory, Springer-Verlag, New York.
- 5. Wang, L., (2005). Support Vector Machines: Theory and Applications. Springer-Verlag, Berlin, Heidelberg.
- 6. Key, J., Maslanik, J.A., and Schweiger, A.J., (1989). Classification of Merged AVHRR and SMMR Arctic Data with Neural Networks. Photogrammetric Engineering and Remote Sensing, 55, 9, 1331-1338.
- 7. Benediktsson, J.A., Swain, P.H., and Ersoy, O.K., (1990). Neural Network Approaches Versus Statistical Methods in Classification of Multisource Remote Sensing Data. IEEE Transactions on Geoscience and Remote Sensing, 28, 4, 540-552.
- 8. Lee, J., Weger, R.C., Sengupta, S.K., and Welch, R.M., (1990). A Neural Network Approach to Cloud Classification. IEEE Transactions on Geoscience and Remote Sensing, 28, 5, 846-855.
- 9. Tso, A. and Mather, P.M., (2009). Classification Methods for Remotely Sensed Data.
- 10. Duda, R.O., Peter, E.H., and Stork, D.G., (2012). Pattern Classification, John Wiley & Sons.
- 11. Schowengerdt, R.A., (2007). Remote Sensing: Models and Methods for Image Processing, Third Edition.
- 12. Kanellopoulos, I. and Wilkinson, G.G., (1997). Strategies and Best Practice for Neural Network Image Classification. International Journal of Remote Sensing, 18, 711-725.
- 13. Foody, G.M., (1995). Land Cover Classification by an Artificial Neural Network with Ancillary Information. International Journal of Geographical Information Systems, 9, 5, 527-542.
- 14. Atkinson, P.M. and Tatnall, A.R.L., (1997). Introduction Neural networks in remote sensing. International Journal of Remote Sensing, 18, 4, 699-709.
- 15. Rumelhart, D.E., Hinton, G.E. and Williams, R.J., (1986). Learning Internal Representations by Error Propagation, Parallel Distributed Processing: Explorations in the Microstruction of Cognition. Rumelhart D.E. and McClelland J.L. (Eds.), Cambridge, MA, The MIT Press. I, 318-362.
- 16. ENVI Field Guide.
- 17. Hsu, C.W., Chang, C.C., and Lin, C.J., (2008). A Practical Guide to Support Vector Classification.
- 18. Congalton, R.G. and Green, K., (1999). Assessing the Accuracy of Remotely Sensed Data: Principles and Practices, Boca Raton, FL: Lewis.
- 19. Yilmaz, V., Konakoglu, B., Serifoglu, C., Gungor, O., and Gökalp, E., (2016). Image Classification-Based Ground Filtering of Point Clouds Extracted from UAV-based Aerial Photos. Geocarto International, 1-11.
- 20. Akar, A., Gökalp, E., Akar, Ö., and Yılmaz, V., (2017). Improving Classification Accuracy of Spectrally Similar Land Covers in the Rangeland and Plateau Areas with a Combination of WorldView-2 and UAV images. Geocarto International, 32, 9, 990-1003.
- 21. http://spatial-analyst.net/ILWIS/htm/ilwismen/confusion_matrix.htm, Access Date: 29.10.2017.
Ayrıntılar
| Birincil Dil | Türkçe |
|---|---|
| Konular | Mühendislik |
| Bölüm | Makaleler |
| Yazarlar | |
| Yayımlanma Tarihi | 23 Temmuz 2018 |
| Yayımlandığı Sayı | Yıl 2018 Cilt: 13 Sayı: 3 |