참고문헌
- 김대성, 김형태 (2008), 누적 유사도 측정을 이용한 자동임계값 결정 기법 - 다중분광 및 초분광영상의 무감독 변화탐지를 목적으로, 대학원격탐사학회지, 대한 원격탐사학회, 제 24권, 제 4호, pp. 341-349.
- 김대성, 김용일, 편무욱 (2011), 구간평균 기법과 직선으로부터의 최대거리를 이용한 초분광영상의 무감독변화탐지, 한국측량학회지, 제 29권, 제 1호, pp. 71- 80.
- Bazi, Y., Bruzzone, L., and Melgani, F. (2007), Image thresholding based on the EM algorithm and the generalized Gaussian distribution, Pattern Recognition archive, Vol. 40, No. 2, pp. 619-634. https://doi.org/10.1016/j.patcog.2006.05.006
- Bruzzone, L., and Prieto, D. F. (2000), Automatic Analysis of the Difference Image for Unsupervised Change Detection, IEEE Transactions on Geoscience and Remote Sensing, IEEE, Vol. 38, No. 3, pp. 1171-1182.
- Carvalho Junior, O. A., Guimares, R. F., Gomes, R. A. T. (2007), Spectral Change Detection, Geoscience and Remote Sensing Symposium, IGARSS 2007. IEEE International Conference on, pp. 1935 - 1938.
- Castellana, L., D'Addabbo, A., and Pasquariello, G. (2007), A Composed Supervised/unsupervised Approach to Improve Change Detection from Remote Sensing, Pattern Recognition Letters, IEEE, Vol. 28, No. 4, pp. 405-413.
- Chang, C. I. (2003), Hyperspectral Imaging - Techniques for Spectral Detection and Classification, Kuwer Academic/Plenum Publishers, New York.
- Eismann, M. T., Meola, J., and Hardie, R. C. (2008), Hyperspectral Change Detection in the Presence of Diurnal and Seasonal Variations, IEEE Transactions on Geoscience and Remote Sensing, IEEE, Vol. 46, No. 1, pp. 237-249.
- Farah, I. R. (2010), Hmissi, S., Ettabaa, K. S., Souleiman, B., Multi-temporal Hyperspectral Images Unmixing and Classification Based on 3D Signature Model and Matching, PIERS ONLINE, Vol. 6, No. 5, pp. 480-484. https://doi.org/10.2529/PIERS091219165514
- Ghosh, S., Mishra, N. S., and Ghosh, A. (2009), Unsupervised Change Detection of Remotely Sensed Images Using Fuzzy Clustering, Advances in Pattern Recognition, ICAPR '09, pp. 385-388.
- Goetz, A. F. H. (1992), Principles of Narrow Band Spectrometry in the Visible and IR: Instuments and Data Analysis. In: F. Ooselli & J. Bodechtel (Eds.), Imaging Spectroscopy: Fundamentals and Prospective Applications, Dordrecht, Kluwer Academic Publishers, pp. 21-32.
- Lowe, D. G. (2004), Distinctive Image Features from Scaleinvariant Keypoints, International Journal on Computer Vision, IJCV, Vol. 60, No. 2, pp. 91-110. https://doi.org/10.1023/B:VISI.0000029664.99615.94
- Lu, D., Mausel, P., brondizio, E., and Moran, E. (2004), Change Detection Techniques, International Journal of Remote Sensing, IJRS, Vol. 25, No. 12, pp. 2365-2407.
- Manolakis, D., Lockwooda, R., Cooleyb, T., and Jacobsonc, J. (2010), Is There a Best Hyperspectral Detection Algorithm?, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XV, Orlando, FL, USA, Vol. 7334.
- Meer, F. V. D. (2006), The Effectiveness of Spectral Similarity Measures for the Analysis of Hyperspectral Imagery, International Journal of Applied Earth Observation and Geoinforrmation, Vol. 8, No. 1, pp. 3-17. https://doi.org/10.1016/j.jag.2005.06.001
- Meola, J., Eismann, M. T., Moses, R. L., and Ash, J. N. (2010), A Model-based Approach to Hyperspectral Change Detection, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVI, Proceedings Vol. 7695.
- Metternicht, G. (1999). Change Detection Assessment using Fuzzy Set and Remotely Sensed Data: an Application of Topographic Map Revision, ISPRS Journal of Photogrammetry and Remote Sensing, ISPRS, Vol. 54, No. 4, pp. 221-233. https://doi.org/10.1016/S0924-2716(99)00023-4
- Nielsen, A. A. (2007), The Regularized Iteratively Reweighted MAD Method for Change Detection in Multiand Hyperspectral Data, IEEE Transactions on Image Processing, Vol. 16, No. 2, pp. 463-478. https://doi.org/10.1109/TIP.2006.888195
- Otsu, N. (1979), A Threshold Selection Method from Graylevel Histograms, IEEE Transactions on Systems, Man, and Cybernetics, IEEE, Vol. 9, pp. 62-66.
- Richard, J. R., Srinivas A., Omar A. and Radrinath R. (2005), Image Change Detection Algorithms: A Systematic Survey, IEEE Transactions on Image Processing, Vol. 14, No. 3, pp. 294-307. https://doi.org/10.1109/TIP.2004.838698
- Rosin, P. L. (2001), Unimodal Thresholding, Pattern Recognition, Vol. 34, pp. 2083-2096. https://doi.org/10.1016/S0031-3203(00)00136-9
- Schaum, A., and Stocker, A. (2004), Advanced Algorithms for Autonomous Hyperspectral Change Detection, the 33rd Applied Imagery Pattern Recognition Workshop (AIPR'04), IEEE Computer Society, pp. 33-38.
- Singh, A. (1989), Digital Change Detection Techniques Using Remotely Sensed Data, International Journal of Remote Sensing, IJRS, Vol. 10, No. 6, pp. 989-1003.
- Sohn, Y., Rebello, N. S. (2002), Supervised and Unsupervised Spectral Angle Classifiers, Photogrammetric Engineering & Remote Sensing, ASPRS, Vol. 68, No. 12, pp. 1271- 1280.
- Vongsy, K., Mendenhall, M. J., Hanna, P. M., and Kaufman, J. (2009), Change Detection Using Synthetic Hyperspectral Imagery, Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, pp. 1-4.
- Wen, X., and Yang, X. (2009), A New Change Detection Method for Two Remote Sensing Images based on Spectral Matching, 2009 International Conference on Industrial Mechatronics and Automation (ICIMA 2009), Chengdu, pp. 89-92.
- Wu, Q. Z., Chen, H. Y., and Jeng, B. S. (2005), Motion Detection via Change-point Detection for Cumulative histograms of ratio images, Pattern Recognition Letters, Vol. 26, pp. 555-563. https://doi.org/10.1016/j.patrec.2004.09.010
피인용 문헌
- Change Detection Using Spectral Unmixing and IEA(Iterative Error Analysis) for Hyperspectral Images vol.31, pp.5, 2015, https://doi.org/10.7780/kjrs.2015.31.5.1