References
- Chang, C.-I., 1999, Spectral Information Divergence for Hyperspectral Image Analysis, International Geoscience and Remote Sensing Symposium. Hamburg, Germany, June 28-July 2, pp.509-511.
- Chang, C.-I., 2000, An Information-theoretic Approach to Spectral Variability Similarity, and Discrimination for Hyperspectral Image Analysis, IEEE Transaction on Information Theory, 46(5): 1927-1932. https://doi.org/10.1109/18.857802
- Chang, A., Y. Kim, S. Choi, D. Han, J. Choi, Y. Kim, Y. Han, H. Park, B. Wang, and H. Lim, 2013, Construction and Data Analysis of Test-bed by Hyperspectral Airborne Remote Sensing, Korean Journal of Remote sensing, 29(2): 161-172. https://doi.org/10.7780/kjrs.2013.29.2.1
- Du, H., C.-I., Chang, H. Ren, C.-C. Chang, J.O. Jensen, and F.M. D'Amico, 2004, New Hyperspectral Discrimination Measure for Spectral Characterization, Optical Engineering, 43(8): 1777-1786. https://doi.org/10.1117/1.1766301
- Fano, R.M., 1961, Transmission of Information: A Statistical Theory of Communication, New York: Wiley.
- Goetz, A.F.H., G. Vane, G. Solomon, J. E, and B.N. Rock, 1985, Imaging Spectrometry for Earth Remote Sensing, Science, 228(4704): 1147-1153. https://doi.org/10.1126/science.228.4704.1147
- Gruninger, J., A.J. Ratkowski, and M.l.L. Hokeb, 2004, The Sequential Maximum Angle Convex Cone(SMACC) Endmember Model, Proceedings SPIE, Algorithms for Multispectral and Hyperspectral and Ultraspectral Imagery, Orlando, FL 5425: 1-14.
- Harsanyi, J.C., and C.-I. Chang, 1994, Hyperspectral Image Classification and Dimensionality Reduction: An Orthogonal Subspace Projection Approach, IEEE Transactions on Geoscience and Remote Sensing, 32(4): 779-785 https://doi.org/10.1109/36.298007
- Jeong, S., C. Park, and S. Kim, Land Cover Classification of The Korean Peninsula Using Linear Spectral Mixture Analysis of MODIS Multi-temporal Data, Korean Journal of Remote sensing, 22(6): 553-563. https://doi.org/10.7780/kjrs.2006.22.6.553
- Kim, K., 2011, A Modified Iterative N-FINDR Algorithm for Fully Automatic Extraction of Endmembers from Hyperspectral Imagery, Korean Journal of Remote Sensing, 27(5): 566-572. https://doi.org/10.7780/kjrs.2011.27.5.565
- Kim, K., 2012, A Study on Fast Extraction of Endmembers from Hyperspectral Image Data, Korean Journal of Remote Sensing, 28(4): 347-355. https://doi.org/10.7780/kjrs.2012.28.4.1
- Kim, S., K. Lee, J. Ma and M. K, 2005, Current Status of Hyperspectral Remote Sensing: Principle, Data Processing Techniques, and Applications, Korean Journal of Remote Sensing, 21(4): 341-369. https://doi.org/10.7780/kjrs.2005.21.4.341
- Lee, J. and K. Lee, 2003, Analysis of Forest Cover Information Extracted by Spectral Mixture Analysis, Korean Journal of Remote Sensing, 19(6): 411-419. https://doi.org/10.7780/kjrs.2003.19.6.411
- Li, H., and L. Zhang, 2011, A Hybrid Automatic Endmembmer Extraction Algorithm Based on Local Window, IEEE Transactions on Geoscience and Remote Sensing, 49(11): 4223-4238. https://doi.org/10.1109/TGRS.2011.2162098
- Neville, R.A., K. Staennz, T. Szeredi, J. Lefebvre, and P. Hauff, 1999, Automatic Endmember Extraction from Hyperspectral Data for Mineral Exploration, Proceedings 21st Canada Symposium on Remote Sensing, Ottawa, ON, Canada, 21-24.
- Plaza, A., P. Martinez, R. Perez, and J. Plaza, 2004, A Quantitative and Comparative Analysis of Endmemeber Extraction Algorithms from Hyperspectral Data, IEEE Transactions on Geoscience and Remote Sensing, 42(3): 650-663. https://doi.org/10.1109/TGRS.2003.820314
- Plaza, A. and C.-I. Chang., 2005, An Improved NFINDR Algorithm in Implementation, Conference on Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XI, SPIE Symposium on Defense and Security, SPIE, Orlando, Florida, 5806: 298-306.
- Plaza. A. and C.-I. Chang., 2006, Impact of Initialization on Design of Endmember Extraction Algorithm, IEEE Transactions on Geoscience and Remote Sensing, 44(11): 3397-3407. https://doi.org/10.1109/TGRS.2006.879538
- Shin, J. and K. Lee, 2012, Comparative Analysis of Target Detection Algorithms in Hyperspectral Image, Korean Journal of Remote Sensing, 28(4): 369-392. https://doi.org/10.7780/kjrs.2012.28.4.3
- Shin, J., S. Kim, J, Yoon, T. Kim and K. Lee, 2006, Spectral Mixture Analysis Using Hyperspectral Image for Hydrological Land Cover Classification in Urban Area, Korean Journal of Remote Sensing, 22(6): 565-574. https://doi.org/10.7780/kjrs.2006.22.6.565
- Van der Meer, F., 2006, The Effectiveness of Spectral Similarity Measures for The Analysis of Hyperspectral Imagery, International Journal of Applied Earth Observation and Geoinformation, 8(1): 3-17. https://doi.org/10.1016/j.jag.2005.06.001
- Winter, M.E., 1999, N-FINDR: An Algorithm for Fast Autonomous Spectral Endmember Determination in Hyperspectral Data, Proceedings SPIE, 3753:266-275.
Cited by
- Spectral Mixture Analysis Using Modified IEA Algorithm for Forest Classification vol.30, pp.2, 2014, https://doi.org/10.7780/kjrs.2014.30.2.5