Abstract
A new method for super-resolution classification from remotely sensed imagery is presented. The method allows prediction of a super-resolution (sub-pixel) land cover map from a coarse spatial resolution (original pixel) land cover proportions image and an intermediate spatial resolution panchromatic (Pan) image. The method is based on spatial simulated annealing and combines two objectives: (i) to match a prior sub-pixel two-point histogram obtained from some training image and (ii) to match the predictions made of an intermediate spatial resolution panchromatic image from the super-resolution classification via a forward model to an observed panchromatic image. The method is demonstrated on simulated remotely sensed imagery. The main advantage of the Pan image is to fix locally the outcome of the two-point histogram objective function.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Adams JB, Smith MO, Johnson PE (1985) Spectral mixture modelling: a new analysis of rock and soil types at the Viking Lander 1 site. J Geophys Res 91:8098–8112
Atkinson PM (1997) Mapping sub-pixel boundaries from remotely sensed images in Innovations in GIS IV. In: Kemp Z (ed). Taylor an1d Francis, London, p 166–180
Atkinson PM (2004) Super-resolution land cover classification using the two-point histogram GeoENV IV: Geostatistics for Environmental Applications. Sánchez-Vila X, Carrera J, Gómez-Hernández J (eds) Kluwer, Dordrecht, pp 15–28
Atkinson PM (2005) Super-resolution target mapping from soft classified remotely sensed imagery. Photogram Eng Remote Sens 71:839–846
Atkinson PM, Cutler MEJ and Lewis H (1997) Mapping sub-pixel proportional land cover with AVHRR imagery. Int J Remote Sens 18: 917–935
Atkinson PM and Tate NJ (2000) Spatial scale problems and geostatistical solutions: a review. Professional Geographer 52:607–623
Bezdek JC, Ehrlich R, Full W (1984) FCM: The fuzzy c-means clustering algorithm. Comput & Geosci 10: 191–203
Support vector machines for optimal classification and spectral unmixing. Ecol Model 120: 167–179
Deutsch CV, Journel AG (1998) GSLIB: Geostatistical Software and User’s Guide Second Edition. Oxford University Press, Oxford
Foody GM (1998) Sharpening fuzzy classification output to refine the representation of sub-pixel land cover distribution. Int J Remote Sens 19: 2593–2599
Hopfield J and Tank DW (1985) Neural computation of decisions in optimization problems. Biol Cybern 52: 141–152
Journel AG and Huijbregts C J (1978) Mining Geostatistics. Academic Press, London
Jupp DLB, Strahler AH and Woodcock CE (1988) Autocorrelation and regularization in digital images I Basic theory. IEEE Trans Geosci Remote Sens 26: 463–473
Mertens KC, Verbeke LPC, Ducheyne EI and De Wulf RR (2003) Using genetic algorithms in sub-pixel mapping. International Journal of Remote Sensing 24: 4241–4247
Mertens KC, Verbeke, LPC Westra T and De Wulf RR (2004) Sub-pixel mapping and sub-pixel sharpening using neural network predicted wavelet coefficients. Remote Sens Environ 91: 225–236
Nguyen MQ, NPM Atkinson and NHG Lewis (2005) Super-resolution mapping using a Hopfield neural network with digital elevation data. NIEEE Trans Geosci Remote Sens 2: 366–370
Nguyen MQ, NPM Atkinson and NHG Lewis (2006) Super-resolution mapping using a Hopfield neural network with fused images. IEEE Trans Geosci Remote Sens 44: 736–749
Steinwendner J, Schneider W and Suppan F (1998) Vector segmentation using spatial subpixel analysis for object extraction. Int Arch Photogram Remote Sens 32: 265–271
Tatem AJ, Lewis HG, Atkinson PM, Nixon MS (2001a) Super-resolution target identification from remotely sensed images using a Hopfield neural network IEEE Trans Geosci Remote Sens 39: 781–796
Tatem AJ, Lewis HG, Atkinson PM, Nixon MS (2001b) Multiple class land cover mapping at the sub-pixel scale using a Hopfield neural network Int J Appl Earth Observation & Geoinf 3: 184–190
Tatem AJ, Lewis HG, Atkinson PM, Nixon MS (2002) Land cover simulation and estimation at the sub-pixel scale using a Hopfield neural network. Remote Sens Environ 79:1–14
Tatem AJ, Lewis HG, Atkinson PM, Nixon MS (2003) Increasing the spatial resolution of landsat TM imagery for land cover mapping in agricultural areas. Int J Geograph Inf Sci 17: 647–672
Thomas IL, Benning VM, Ching NP (1987) Classification of Remotely Sensed Images. Adam Hilger, Bristol
Thornton MW, Atkinson PM, Holland DA (2006) Super-resolution mapping of rural land cover features from fine spatial resolution satellite sensor imagery. Int J Remote Sens 27: 473–491
Woodcock CE, Strahler AH (1987) The factor of scale in remote sensing. Remote Sens Environ 21: 311–322
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2008 Springer Science+Business Media B.V.
About this chapter
Cite this chapter
Atkinson, P.M. (2008). Super-Resolution Mapping Using the Two-Point Histogram and Multi-Source Imagery. In: Soares, A., Pereira, M.J., Dimitrakopoulos, R. (eds) geoENV VI – Geostatistics for Environmental Applications. Quantitative Geology and Geostatistics, vol 15. Springer, Dordrecht. https://doi.org/10.1007/978-1-4020-6448-7_26
Download citation
DOI: https://doi.org/10.1007/978-1-4020-6448-7_26
Publisher Name: Springer, Dordrecht
Print ISBN: 978-1-4020-6447-0
Online ISBN: 978-1-4020-6448-7
eBook Packages: Earth and Environmental ScienceEarth and Environmental Science (R0)