Enhanced self-training superresolution mapping technique for hyperspectral imagery

IEEE Geoscience and Remote Sensing Letters

Volumn 8, Issue 4, 2011, Pages 671-675

  Mianji, Fereidoun A ^a   Gu, Yanfeng ^a   Zhang, Ye ^a   Zhang, Junping ^a  

^a HARBIN INSTITUTE OF TECHNOLOGY   (China)

Author keywords

Data fusion; hyperspectral (HS) imagery; key information detection; linear mixture model (LMM); superresolution mapping (SRM)

Indexed keywords

CONSTRAINED LEAST SQUARES; HIGH RESOLUTION; HYPERSPECTRAL; HYPERSPECTRAL IMAGERY; INFORMATION DETECTION; LINEAR MIXTURE MODEL (LMM); LINEAR MIXTURE MODELS; LOW COMPLEXITY; MAPPING MODEL; SELF-TRAINING; SPATIAL CORRELATION MODELS; SPECTRAL CONTENT; SUPERRESOLUTION MAPPING; SUPERRESOLUTION TECHNIQUE; UNMIXING;

ALGORITHMS; DATA FUSION; INFORMATION FUSION; OPTICAL RESOLVING POWER; REMOTE SENSING;

MAPPING;

EID: 79959741708     PISSN: 1545598X     EISSN: None     Source Type: Journal    
DOI: 10.1109/LGRS.2010.2102334     Document Type: Article

References (15)

1. M. T. Eismann and R. C. Hardie, "Hyperspectral resolution enhancement using high-resolution multispectral imagery with arbitrary response functions," IEEE Trans. Geosci. Remote Sens., vol. 43, no. 3, pp. 455-465, Mar. 2005. (Pubitemid 40320268)
2. G. M. Foody, "Sharpening fuzzy classification output to refine the representation of sub-pixel landcover distribution," Int. J. Remote Sens., vol. 19, no. 13, pp. 2593-2599, 1998. (Pubitemid 28560708)
3. M. Q. Nguyen, P. M. Atkinson, and H. G. Lewis, "Superresolution mapping using a Hopfield neural network with fused images," IEEE Trans. Geosci. Remote Sens., vol. 44, no. 3, pp. 736-749, Mar. 2006. (Pubitemid 43282748)
4. Y. Gu, Y. Zhang, and J. Zhang, "Integration of spatial-spectral information for resolution enhancement in hyperspectral images," IEEE Trans. Geosci. Remote Sens., vol. 46, no. 5, pp. 1347-1358, May 2008.
5. A. J. Tatem, H. G. Lewis, P. M. Atkinson, and M. S. Nixon, "Superresolution target identification from remotely sensed images using a Hopfield neural network," IEEE Trans. Geosci. Remote Sens., vol. 39, no. 4, pp. 781-796, Apr. 2001. (Pubitemid 32432734)
6. Y. Ge, S. Li, and V. C. Lakhan, "Development and testing of a subpixel mapping algorithm," IEEE Trans. Geosci. Remote Sens., vol. 47, no. 7, pp. 2155-2164, Jul. 2009.
7. A. Plaza, P. Martinez, R. Perez, and J. Plaza, "A quantitative and comparative analysis of endmember extraction algorithms from hyperspectral data," IEEE Trans. Geosci. Remote Sens., vol. 42, no. 3, pp. 650-663, Mar. 2004.
8. J. W. Boardman, F. A. Kruse, and R. O. Green, "Mapping target signatures via partial unmixing of AVIRIS data," in Proc. Summaries VI JPL Airborne Earth Sci. Workshop, Pasadena, CA, 1995, pp. 23-26.
9. J. Bolton and P. Gader, "Random set framework for context-based classification with hyperspectral imagery," IEEE Trans. Geosci. Remote Sens., vol. 47, no. 11, pp. 3810-3821, Nov. 2009.
10. S. K. Das and R. Singh, "Performance of kriging-based soft classification on WiFS-IRS-1D image using ground hyperspectral signatures," IEEE Geosci. Remote Sens. Lett., vol. 6, no. 3, pp. 453-457, Jul. 2009.
11. N. Dobigeon, J. Y. Tourneret, and C. I. Chang, "Semi-supervised linear spectral unmixing using a hierarchical Bayesian model for hyperspectral imagery," IEEE Trans. Signal Process., vol. 56, no. 7, pp. 2684-2695, Jul. 2008.
12. D. Heinz and C. I. Chang, "Fully constrained least squares linear spectral mixture analysis method for material quantification in hyperspectral imagery," IEEE Trans. Geosci. Remote Sens., vol. 39, no. 3, pp. 529-545, Mar. 2001. (Pubitemid 32400422)
13. J. A. Richards and X. Jia, "Using suitable neighbors to augment the training set in hyperspectral maximum likelihood classification," IEEE Geosci. Remote Sens. Lett., vol. 5, no. 4, pp. 774-777, Oct. 2008.
14. K. Hornik, M. Stinchcombe, and H. White, "Multilayer feedforward networks are universal approximators," Neural Netw., vol. 2, no. 5, pp. 359-366, 1989. (Pubitemid 20609008)
15. F. A. Mianji, Y. Zhang, and A. Babakhani, "Key information retrieval in hyperspectral imagery through spatial-spectral data fusion," Radioeng., vol. 19, no. 4, pp. 734-744, Dec. 2010.