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Increasing Spatial Resolution of Remotely Sensed Image using HNN Super-resolution Mapping Combined with a Forward Model
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 Title & Authors
Increasing Spatial Resolution of Remotely Sensed Image using HNN Super-resolution Mapping Combined with a Forward Model
Minh, Nguyen Quang; Huong, Nguyen Thi Thu;
Spatial resolution of land covers from remotely sensed images can be increased using super-resolution mapping techniques for soft-classified land cover proportions. A further development of super-resolution mapping technique is downscaling the original remotely sensed image using super-resolution mapping techniques with a forward model. In this paper, the model for increasing spatial resolution of remote sensing multispectral image is tested with real SPOT 5 imagery at 10m spatial resolution for an area in Bac Giang Province, Vietnam in order to evaluate the feasibility of application of this model to the real imagery. The soft-classified land cover proportions obtained using a fuzzy c-means classification are then used as input data for a Hopfield neural network (HNN) to predict the multispectral images at sub-pixel spatial resolution. The 10m SPOT multispectral image was improved to 5m, 3,3m and 2.5m and compared with SPOT Panchromatic image at 2.5m resolution for assessment.Visually, the resulted image is compared with a SPOT 5 panchromatic image acquired at the same time with the multispectral data. The predicted image is apparently sharper than the original coarse spatial resolution image.
Hopfield neural network optimization;Soft classification;Image downscaling;Forward model;
 Cited by
Akgun, T., Y., A., and Mersereau, R., ( 2005), Superresolution reconstruction of hyperspectral images, IEEE Transactions on Image Processing, 14, pp. 1860-1875. crossref(new window)

Atkinson, P. M., (1997), Mapping sub-pixel boundaries from remotely sensed images. In Z. K. (Ed.) (Ed.), Innovation in GIS 4 (pp. 166-180), London: Taylor and Francis.

Atkinson, P., Pardo-Iguzquiza, E., and Chica-Olmo, M., (2008), Downscaling Cokriging for Super-Resolution Mapping of Continua in Remotely Sensed Images, IEEE Transactions on Geoscience and Remote Sensing, 46(2), pp. 573 - 580. crossref(new window)

Bezdek, J. C., Keller, J. M., Krishnapuram, R., and and Pal, N. R., (1999), Fuzzy Models and Algorithms for Pattern Recognition and Image Processing, Boston: Kluwer.

Elad, M., and Feuer, A., (1999), Super-resolution reconstruction of image sequences, IEEE Transactions on Pattern Analysis and Machine Intelligence, 21, pp. 817-834. crossref(new window)

Foody, G. M., (2002a), Hard and soft classifications by a neural network with a nonexhaustively defined set of classes, International Journal of Remote Sensing, 23, pp. 3853-3864. crossref(new window)

Foody, G. M., (2002b), The role of soft classification techniques in the refinement of estimates of Ground Control Point location, Photogrammetric Engineering and Remote Sensing, 68, pp. 897-903.

Kyriakidis, P. C., and Yoo, E. H., (2005), Geostatistica prediction and simulation of point values from the areal data, Geographical Analysis, 37, pp. 124-152. crossref(new window)

Ling, F., Du, Y., Xiao, F., Xue, H., and Wu, S. J., (2010), Superresolution land-cover mapping using multiple sub-pixel shifted remotely sensed images, International Journal of Remote Sensing, 31(19), pp. 5023-5040. crossref(new window)

Merterns, K. C., Verbeke, L. D., and De Wulf, R., (2003), Using genetic algorithms in sub-pixel mapping, International Journal of Remote Sensing, 24, pp. 4241-4247. crossref(new window)

Merterns, K. C., Verbeke, L., Westra, T., and De Wulf, R., ( 2004), Sub-pixel mapping and sub-pixel sharpening using neural network predicted wavelet coefficients, Remote Sensing of Environment, 91(2), pp. 225-236. crossref(new window)

Nguyen, Q. M., Atkinson, P. M., and Lewis, H. G., (2006), Super-resolution mapping using Hopfield neural netwok with fused images, IEEE Transactions on Geoscience and Remote Sensing, 44(3), pp. 736-749. crossref(new window)

Nguyen, Q. M., Atkinson, P. M., and Lewis, H. G., (2011), Super-resolution mapping using Hopfield Neural Network with panchromatic imagery, International Journal of Remote Sensing, 32(21), pp. 6149-6176. crossref(new window)

Nguyen, Q. M., Do, V. D., Atkinson, P., and H.G.Lewis., (2009), Downscaling Multispectral Imagery Based on the HNN Using Forward Model, 7th FIG Regional Conference on Spatial Data Serving People: Land Governance and the Environment-Building the Capacity, Hanoi.

Pinilla Ruiz, C., and Ariza Popez F.J., (2002), Restoring SPOT images using PSF-derived deconvolution filters, International Journal of Remote Sensing, 23, pp. 2379-2391. crossref(new window)

Schneider, W., and Steinwendner, J., (1999), Land cover mapping by interrelated segmentation and classification of satellite images, International Archives of Photogrammetry and Remote Sensing, 32, part 7-4-3.

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. In F. G. M., and A. P. M. (Eds.), Uncertainty in Remote Sensing and GIS (pp. 59-76.). London: John Wiley and Sons.

Tipping, M. E., and Bishop, C. M., (2003), Bayesian image super-resolution in Advances in Neural Information Processing Systems, Boston: The MIT Press.

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. crossref(new window)