Super-resolution mapping using hopfield neural network with panchromatic imagery

International Journal of Remote Sensing

Volumn 32, Issue 21, 2011, Pages 6149-6176

  Nguyen, Quang Minh ^a   Atkinson, Peter M ^b   Lewis, Hugh G ^b  

^a HANOI UNIVERSITY OF MINING AND GEOLOGY   (Viet Nam)

^b UNIVERSITY OF SOUTHAMPTON   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

HOPFIELD NEURAL NETWORKS; IMAGE RESOLUTION; OPTICAL RESOLVING POWER; PIXELS; REFLECTION;

ENERGY FUNCTIONS; HOPFIELD NEURAL NETWORKS (HNN); PROPORTION IMAGE; SOFT CLASSIFICATION; SPATIAL RESOLUTION; SUPER-RESOLUTION MAPPINGS; SUPPLEMENTARY INFORMATION; WEIGHTING FACTORS;

MAPPING;

ACCURACY ASSESSMENT; ARTIFICIAL NEURAL NETWORK; COMPUTER SIMULATION; IMAGE CLASSIFICATION; LAND COVER; MAPPING; PANCHROMATIC IMAGE; PIXEL; SPATIAL RESOLUTION;

EID: 82055172125     PISSN: 01431161     EISSN: 13665901     Source Type: Journal    
DOI: 10.1080/01431161.2010.507797     Document Type: Article

References (22)

1. Aplin, P. and Atkinson, P.M., 2001, Sub-pixel land cover mapping for per-field classification. International Journal of Remote Sensing, 22, pp. 2853-2858.
2. Atkinson, P.M., 1997, Mapping sub-pixel boundaries from remotely sensed images. In Innovation in GIS 4, Z. Kemp (Ed.), pp. 166-180 (London: Taylor & Francis).
3. Atkinson, P.M., 2008, Super-resolution mapping using the two-point histogram and multi-source imagery. In geoENV VI - Geostatistics for Environmental Applications, A. Soares, M.J. Pereira and R. Dimitrakopoulos, (Eds.), pp. 307-321 (Dordrecht: Springer).
4. Bastin, L., 1997, Comparison of fuzzy c-means classification, linear mixture modelling and MLC probabilities as tools for unmixing coarse pixels. International Journal of RemoteSensing, 18, pp. 3629-3648.
5. Boucher, A. and Kyriakidis, P.C., 2006, Super-resolution land cover mapping with indicator geostatistics. Remote Sensing of Environment, 104, pp. 264-282.
6. Brown, M., Lewis, H. and Gunn, S., 2000, Linear spectral mixture models and support vector machines for remote sensing. IEEE Transactions on Geoscience and Remote Sensing, 38,pp. 2346-2360.
7. Carpenter, G.M., Gopal, S., Macomber, S., Martens, S. and Woodcock, C.E., 1999, Aneural network method for mixture estimation for vegetation mapping. Remote Sensing of Environment, 70, pp. 138-152.
8. Foody, G.M., 2004, Sub-pixel methods in remote sensing. In Remote Sensing Image Analysis, S.M. de Jong and F. van der Meer (Eds.), pp. 37-49 (Dortrecht: Kluwer Academic).
9. Foody, G.M., Lucas, R.M., Curran, P.J. and Honzak, M., 1997, Non-linear mixture modelling without end-members using an artificial neural network. International Journal of Remote Sensing, 18, pp. 937-953.
10. Mertens, K.C., Verbeke, L.P.C., Ducheyne, E.I. and De Wulf, R.R., 2003, Using genetic algorithms in sub-pixel mapping. International Journal of Remote Sensing, 24, pp. 4241-4247.
11. Mertens, K.C., Verbeke, L.P.C., Westra, T. and De Wulf, R.R., 2004, Sub-pixel mapping and sub-pixel sharpening using neural network predicted wavelet coefficients. Remote Sensing of Environment, 91, pp. 225-236.
12. Nguyen, Q.M., Atkinson, P.M. and Lewis, H.G., 2005, Super-resolution mapping using Hopfield neural network with LiDAR data. IEEE Geoscience and Remote Sensing Letters, 2, pp. 366-370.
13. Nguyen, Q.M., Atkinson, P.M. and Lewis, H.G., 2006, Super-resolution mapping using Hopfield neural network with fused images. IEEE Transactions on Geoscience and Remote Sensing, 44, pp. 736-749.
14. Schowengerdt, R.A., 1996, On the estimation of spatial-spectral mixing with classifier likelihood functions. Pattern Recognition Letters, 17, pp. 1379-1387.
15. Schowengerdt, R.A., 1997, Remote Sensing: Models and Methods for Image Processing, pp. 389-470 (San Diego, CA: Academic Press).
16. Settle, J.J. and Drake, N.A., 1993, Linear mixing and the estimation of ground cover proportions. International Journal of Remote Sensing, 14, pp. 1159-1177.
17. Tatem, A.J., Lewis, H.G., Atkinson, P.M. and Nixon, M.S., 2001a, Super-resolution target identification from remotely sensed images using a Hopfield neural network. IEEE Transactions on Geoscience and Remote Sensing, 39, pp. 781-796.
18. Tatem, A.J., Lewis, H.G., Atkinson, P.M. and Nixon, M.S., 2001b, Multi-class land cover mapping at the sub-pixel scale using a Hopfield neural network. International Journal of Applied Earth Observation and Geoinformation, 3, pp. 184-190.
19. Tatem, A.J., Lewis, H.G., Atkinson, P.M. and Nixon, M.S., 2002, Super-resolution land cover pattern prediction using a Hopfield neural network. Remote Sensing of Environment, 79, pp. 1-14.
20. Verhoeye, J. and De Wulf, R., 2002, Land cover mapping at sub-pixel scales using linear optimisation techniques. Remote Sensing of Environment, 79, pp. 96-104.
21. Zhang, L., Wu, K., Zhong, Y. and Li, P., 2008, A new sub-pixel mapping algorithm based on a BP neural network with an observation model. Neurocomputing, 71, pp. 2046-2054.
22. Zhang, Y., 1999, A new merging method and its spectral and spatial effects. International Journal of Remote Sensing, 20, pp. 2003-2014.