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Land Cover Super-resolution Mapping using Hopfield Neural Network for Simulated SPOT Image
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 Title & Authors
Land Cover Super-resolution Mapping using Hopfield Neural Network for Simulated SPOT Image
Nguyen, Quang Minh;
 
 Abstract
Using soft classification, it is possible to obtain the land cover proportions from the remotely sensed image. These land cover proportions are then used as input data for a procedure called "super-resolution mapping" to produce the predicted hard land cover layers at higher resolution than the original remotely sensed image. Superresolution mapping can be implemented using a number of algorithms in which the Hopfield Neural Network (HNN) has showed some advantages. The HNN has improved the land cover classification through superresolution mapping greatly with the high resolution data. However, the super-resolution mapping is based on the spatial dependence assumption, therefore it is predicted that the accuracy of resulted land cover classes depends on the relative size of spatial features and the spatial resolution of the remotely sensed image. This research is to evaluate the capability of HNN to implement the super-resolution mapping for SPOT image to create higher resolution land cover classes with different zoom factor.
 Keywords
Hopfield Neural Network;Super-resolution Mapping;Land cover classification;Spatial Dependence;
 Language
English
 Cited by
1.
Application of Remote Sensing and GIS technology for monitoring coastal changes in estuary area of the Red river system, Vietnam,;;;;;

한국측량학회지, 2013. vol.31. 6_2, pp.529-538 crossref(new window)
 References
1.
Atkinson, P. M., 1997. Mapping sub-pixel boundaries from remotely sensed images.. In: Z. K. (Ed.), ed. Innovation in GIS 4. London: Taylor & Francis, pp. 166-180.

2.
Atkinson, P. M., 2008. Super-Resolution Mapping Using the Two-Point Histogram and Multi-Source Imagery. In: A. S. e. al, ed. geoENV VI - Geostatistics for Environmental Applications. Netherlands: Springer , pp. 307-321.

3.
Bast in, L., 1997. Comparison of fuzzy c-means classification, linear mixture modelling and MLC probabilities as tools for unmixing coarse pixels. International Journal of Remote Sensing, pp. 3629-3648.

4.
Brown, M., Lewis, H. & Gunn, S., 2000. Linear spectral mixture models and Support Vector Machines for Remote Sensing. IEEE Transactions on Geoscience and Remote Sensing, pp. 2346-2360.

5.
Foody, G. M., 2004. Sub-pixel methods in Remote Sensing. In: Remote Sensing Image Analysis. Dortrecht: Kluwer Academic Publisher, pp. 37-49.

6.
Foody, G. M., Lucas, R. M., Curran, P. J. & Honzak, M., 1997. Non-linear mixture modelling without end-members using an artificial neural network.. International Journal of Remote Sensing, pp. 937-953..

7.
Ling, F. et al., 2010. Super-resolution land-cover mapping using multiple sub-pixel shifted remotely sensed images. International Journal of Remote Sensing, 31(19), pp. 5023-504. crossref(new window)

8.
Mehrotra, K., Mohan, K. C. & S., R., 1997. Elements of Artificial Neural Networks. Cambridge, Massachusetts: The MIT press.

9.
Mertens, K. C., Verbeke, L. P. C., I., D. E. & De Wulf, R. R., 2003. Using genetic algorithms in sub-pixel mapping. International Journal of Remote Sensing, Volume 24, pp. 4241-4247. crossref(new window)

10.
Minh, N. Q., 2006. PhD thesis: Super-resolution mapping using Hopfield Neuron Network with supplementary data, Southampton: Southampton Library.

11.
Muad, A. & Foody, G., 2010. Super-resolution mapping using multiple observations and Hopfield neural network. Toulouse, France, Proceedings of the SPIE Remote Sensing.

12.
Schowengert, R. A., 1996. On the estimation of spatialspectral mixing with classifier likelihood functions. Pattern Recognition Letter, pp. 1379-1387.

13.
Settle, J. J. & Drake, N. A., 1993. Linear mixing and the estimation of ground cover proportions. International Journal of Remote Sensing, pp. 1159-1177.

14.
Tatem, A. J., Lewis, H. G., Atkinson, P. M. & Nixon, M. S., 2001. Multi-class land cover mapping at the sub-pixel scale using a Hopfield neural network. International Journal of Applied Earth Observation and Geoinformation, Volume 3, pp. 184-190. crossref(new window)

15.
Verhoeye, J. & De Wulf, R., 2002. Remote Sensing of Environment. Land cover mapping at sub-pixel scales using linear optimisation techniques, Volume 79, pp. 96-104.

16.
Zhang, L., Wu, K., Zhong, Y. & Li, P., 2008. A new subpixel mapping algorithm based on a BP neural network with an observation model. Neurocomputing, Volume 71, pp. 2046-2054. crossref(new window)