. .   , 2003 . 17, . 7, 647–672 Research Article Increasing the spatial resolution of agricultural land cover maps using a Hopfield neural network A. J. TATEM TALA Research Group, Department of Zoology, Oxford University, South Parks Road, Oxford OX1 3PS, England, UK e-mail: Andy.Tatem@zoology.oxford.ac.uk H. G. LEWIS Department of Aeronautical and Astronautical Engineering, University of Southampton, Southampton SO17 1BJ, England, UK P. M. ATKINSON Department of Geography, University of Southampton, Southampton SO17 1BJ, England, UK and M. S. NIXON Department of Electronics and Computer Science, University of Southampton, Southampton SO17 1BJ, England, UK (Received 12 July 2002; accepted 20 January 2003 ) Abstract. Land cover class composition of remotely sensed image pixels can be estimated using soft classification techniques increasingly available in many GIS packages. However, their output provides no indication of how such classes are distributed spatially within the instantaneous field of view represented by the pixel. Techniques that attempt to provide an improved spatial representation of land cover have been developed, but not tested on the difficult task of mapping from real satellite imagery. The authors investigated the use of a Hopfield neural network technique to map the spatial distributions of classes reliably using information of pixel composition determined from soft classification previously. The approach involved designing the energy function to produce a ‘best guess’ prediction of the spatial distribution of class components in each pixel. In previous studies, the authors described the application of the technique to target identifica- tion, pattern prediction and land cover mapping at the sub-pixel scale, but only for simulated imagery. We now show how the approach can be applied to Landsat Thematic Mapper (TM) agriculture imagery to derive accurate estimates of land cover and reduce the uncertainty inherent in such imagery. The technique was applied to Landsat TM imagery of small-scale agriculture in Greece and large- scale agriculture near Leicester, UK. The resultant maps provided an accurate and improved representation of the land covers studied, with RMS errors for the Landsat imagery of the order of 0.1 in the new fine resolution map recorded. The results showed that the neural network represents a simple efficient tool for International Journal of Geographical Information Science ISSN 1365-8816 print/ISSN 1362-3087 online © 2003 Taylor & Francis Ltd http://www.tandf.co.uk/journals DOI: 10.1080/1365881031000135519