Skip to main content
Article thumbnail
Location of Repository

Super-resolution target identification from remotely sensed images using a Hopfield neural network

By A.J. Tatem, H.G. Lewis, P.M. Atkinson and M.S. Nixon


Fuzzy classification techniques have been developed recently to estimate the class composition of image pixels, but their output provides no indication of how these classes are distributed spatially within the instantaneous field of view represented by the pixel. As such, while the accuracy of land cover target identification has been improved using fuzzy classification, it remains for robust techniques that provide better spatial representation of land cover to be developed. Such techniques could provide more accurate land cover metrics for determining social or environmental policy, for example. The use of a Hopfield neural network to map the spatial distribution of classes more reliably using prior information of pixel composition determined from fuzzy classification was investigated. An approach was adopted that used the output from a fuzzy classification to constrain a Hopfield neural network formulated as an energy minimization tool. The network converges to a minimum of an energy function, defined as a goal and several constraints. Extracting the spatial distribution of target class components within each pixel was, therefore, formulated as a constraint satisfaction problem with an optimal solution determined by the minimum of the energy function. This energy minimum represents a “best guess” map of the spatial distribution of class components in each pixel. The technique was applied to both synthetic and simulated Landsat TM imagery, and the resultant maps provided an accurate and improved representation of the land covers studied, with root mean square errors (RMSEs) for Landsat imagery of the order of 0.09 pixels in the new fine resolution image recorde

Topics: T1, GA, QA75
Year: 2001
OAI identifier:
Provided by: e-Prints Soton

Suggested articles


  1. (1990). A theoretical investigation into the performance of the Hopfield model,”
  2. (1998). Algorithmic improvements in spatial subpixel analysis ofremote sensing images,” in
  3. (1997). Color image segmentation using Hopfield networks,”
  4. (1999). Detection and location of objects from mobile mapping image sequences by Hopfield neural networks,”
  5. (1999). Fine spatial resolution simulated satellite imagery for land cover mapping in the United Kingdom,” Remote Sens.
  6. (1999). From satellite images to scene description using advanced image processing techniques,” in
  7. (1998). Geostatistics and remote sensing,” Progress Phys.
  8. (1993). Global convergence and suppression of spurious states of the Hopfield neural networks,”
  9. (1992). Hopfield network for stereo vision correspondence,”
  10. Introduction to Remote Sensing, 2nd ed.
  11. (1997). J.SteinwendnerandW.Schneider,“Aneuralnetapproachtospatialsubpixelanalysisinremotesensing,”inProc.21stWorkshopoftheAustrian Association for Pattern Recognition,
  12. (1999). Land cover mapping from optical satellite images employing subpixel segmentation and radiometric calibration,” in Machine Vision and Advanced Image Processing in Remote
  13. (1993). Land use mapping with subpixel accuracy from landsat TM image data,” in
  14. Les variables régionalisées et leur estimation,
  15. (1996). Linear spectral mixture modeling to estimate vegetation amount from optical spectral data,”
  16. (1997). Mapping subpixel boundaries from remotely sensed images,”inInnovationsinGISIV,Z.Kemp,Ed. London,U.K.:Taylor and Francis,
  17. (1997). Mapping subpixel proportional land cover with AVHRR imagery,”
  18. (1985). Neural computation of decisions in optimization problems,”
  19. (1984). Neurons with graded response have collective computational properties like those of two-state neurons,” in
  20. (1999). Posing structural matching in remote sensing as an optimization problem,” presented at the
  21. (1997). Remote Sensing: Models and Methods for Image Processing.
  22. (1996). SegmentationofGabor-filtered textures using deterministic relaxation,”
  23. (1998). Sharpening fuzzy classification output to refine the representation of sub-pixel land cover distribution,”
  24. (1999). Support vector machines for optimal classification and spectral unmixing,”
  25. (1987). The factor of scale in remote sensing,” Remote Sens.
  26. (1997). The Hopfield neural network as a tool for feature tracking and recognition from satellite sensor images,”
  27. (1997). The pixel: A snare and a delusion,”
  28. (1998). The Use of Shape, Appearance and the Dynamics of Clouds for Satellite Image Interpretation,” Ph.D. Thesis,
  29. (1998). Vector segmentation using spatial subpixel analysis for object extraction,”

To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.