Feature extraction in high-resolution raster images using neural networks

• Autorzy: Iwaniak A. , Kubik T. , Paluszyński W. , Tymków P.
• Konferencja: Bilateral Geodetic Meeting Poland-Italy (8th; 22-24.06.2006; Wrocław, Poland)
• Języki publikacji: EN

Słowa kluczowe

EN

GIS project; neural network

PL

GIS projekt; sieci neuronowe

• Wydawca: Wydział Geodezji i Kartografii Politechniki Warszawskiej
• Czasopismo: Reports on Geodesy
• Rocznik: 2006
• Tom: z. 2/77
• Strony: 263--271
• Opis fizyczny: Bibliogr. 21poz., schem., tab., rys.

Twórcy

autor

Iwaniak A.

autor

Kubik T.

autor

Paluszyński W.

autor

Tymków P.

• Agricultural University of Wrocław Poland

Bibliografia

• 1. Barr, S. and Barnsley, M.J., 1999. A syntactic pattern recognition paradigm for the derrivation of second-order thematic Information from remotely sensed images. In: P.M. Atkinson and N.J. Tate (Editors), Advances in Remote Sensing and GIS Analysis. Chichester: Wiley, pp. 167-184.
• 2. Berberoglu, S., Lloyd, C.D., Atkinson, P.M. and Curran, P.J., 2000. The integration of spectral and textural information using neural networks for land cover mapping in the Mediterranean. Computers and Geosciences, 26(4): 385-396.
• 3. Carr, J.R., 1996. Spectral and textural classification of single and multiple band digital images. Computers and Geosciences, 22(8):849-865.
• 4. Clausi, D.A. and Zhao, Y., 2002. Rapid extraction of image texture by co-occurrence using a hybrid data structure. Computers and Geosciences, 28(6): 763-774.
• 5. Cohen, J., 1960. A Coefficient of Agreement for Nominal Scales. Educational and Psychological Measurement, 20: 37-46.
• 6. Cohen, J., 1968. Weighted kappa: nominal scale agreement with provision for scale disagreement or partial credit. Psychol. Bull., 70: 213-220.
• 7. German, G.W.H. and Gahegan, M.N., 1996. Neural network architectures for the classification of temporal image sequences. Computers and Geosciences, 22(9): 969-979.
• 8. Haralick, R.M., 1979. Statistical and structural approaches to texture. Proceedings of IEEE, 67(5): 786-803.
• 9. Haralick, R.M. and Shapiro, L.G., 1992. Computer and Robot Vision, vol. 1., 1. Addison-Wesley Publishing, Reading, MA.
• 10. Hubert-Moy, L., Cotonnec, A., Du, L.L., Chardin, A. and Perez, P., 2001. A Comparison of Parametric Classification Procedures of Remotely Sensed Data Applied on Different Landscape Units. Remote Sensing of Environment, 75(2): 174-187.
• 11. Iwaniak A., Krówczyńska M., Pałuszynski W., Using Neural Networks for Urban Area Classification in Satellite Images, EARSEL, Ghent, Belgium 2003.
• 12. Iwaniak A., Kubik T., Paluszyński W., Tymków P., Classification of Features in High-resolution Aerial Pnotographs Using Neural Networks, w: XXII International Cartographic Conference (ICC'2005) , ISBN:0-958-46093-0, A Coruna 2005.
• 13. Liu, X.-H., Skidmore, A.K. and H. Van Oosten, 2002. Integration of Classification methods for improvement of land-cover map accuracy. ISPRS Journal of Photogrammetry and Remote Sensing, 56(4):257-268.
• 14. Meng, L., 1993. Application of neural network in cartographic pattern recognition, Proceedings 16th International Cartographic Conference, Cologne, pp. 192-202.
• 15. Miller, D.M., Kaminsky, EJ. and Rana, S., 1995. Neural network Classification of remote-sensing data. Computers and Geosciences, 21(3): 377-386.
• 16. Müller, J.-C., 1992. Parallel distributed processing: An application to geographic feature selection, Proceedings Fifth International Symposium on Spatial Data Handling, Charleston, SC, pp. 230-240.
• 17. Openshaw, S., Wymer, C. and Cross, A., 1991. Using neural nets to solve some hard analysis problems in GIS, Proceedings EGIS '91, Brussels, pp. 797-807.
• 18. Ryherd, S. and Woodcock, C., 1996. Combining spectral and texture data in the segmentation of remotely sensed images. Photogramm. Eng. Remote Sens., 62: 181-194.
• 19. Tatem, A.J., Lewis, H.G., Atkinson, P.M. and Nixon, M.S., 2001. Super-Resolution Target Identification from Remotely Sensed Images using a Hopfield Neural Network. IEEE Transactions on Geoscience and Remote Sensing (39): 781-796.
• 20. Venkatesh, Y.Y. and S. Kumar Raja, 2003. On the classification of multispectral satellite images using the multilayer perceptron. Pattern Recognition, 36(9): 2161-2175.
• 21. Zhang, Y., 1999. Optimisation of building detection in satellite images by combining multispectral classification and texture filtering. ISPRS Journal of Photogrammetry and Remote Sensing, 54(1):50-60.