Abstract：Hyperspectral imagery (HSI) is used in more and more fields, but its low spatial resolution limits its applications severely. The super-resolution algorithm catches more and more eyes but has not been solved well. In this case, the present paper aimed to do the following researches. The relation modeling was constructed between observed HSI of low resolution and target HSI of high resolution. In the modeling, space transformation was implemented by introducing the operator related to endmembers (EMs) of interest. Maximum posterior probability (MAP) algorithm was used to realize the super-resolution (SR) recovery. Experiments show that the proposed SR method has good recovery effect, low computational complexity, robust noise resistance, and can preserve classes of interest.
Key words：Hyperspectral imagery(HSI);Super-resolution;Maximum a posterior probability (MAP);Endmember
[1] Harris J L. Journal of the Optical Society of America，1964, 54(7)：31. [2] Huang T S, Tsai R. Advances in Computer Vision and Image Processing，1984, 10(2)：317. [3] Henry Stark, Peyma Oskoui. Journal of the Optica Society of America, 1989, B6(11): 115. [4] Rusforth C K. In Image Reconstruction, Theory and Application. New York: Academic Press, 1987. 313. [5] Richardson W H. Journal of the Optica Society of America, 1972, 62(1)：55. [6] Wald L, Ranchin T, Mangolini M. Photogrammetric Engineering & Remote Sensing, 1997, 63(6)：691. [7] Yocky D A A. Photogram. Engin. & Remote Sensing, 1996, 62(9): 1067. [8] Aiazzi B, Alparone L, Barducci A. IEEE International Geoscience and Remote Sensing Symposium, 1999, 2：1183. [9] Gavin J, Jennison C. J. Computational and Graphical Statistics, 1997, 6：182. [10] Tatem A J, Lewis H G, Atkinson P M, et al. Remote Sensing of Environment, 2002, 79: 1. [11] Atkinson P M. Innovations in Geographic Information System, 1997, 4: 166. [12] Verhoeye J，Robert D W. Remote Sensing of Environment，2002, 79(1)：96. [13] HU Min, Tan Jieqing，ZHAO Qianjin. Journal of Information and Computational Science, 2007，4(1): 59. [14] Abma Ray, Kabir Nurul. Geophysics, 2006，71(6)：E91. [15] Jeng Sheng-Wen, Tsai Wen-Hsiang. Pattern Recognition Letters, 2007，28(15)：1926. [16] Vapnik V N. The Nature of Statistical Learning Theory. New York: Springer Press, 1995.
