On the Ambiguity Induced by a Remote Sensor's PSF

Uncertainty in Remote Sensing and GIS

Volumn , Issue , 2006, Pages 37-57

  Manslow, J F ^a   Nixon, M S ^a  

^a UNIVERSITY OF SOUTHAMPTON   (United Kingdom)

Author keywords

Conditional distribution representation; Empirical statistical technique; Gaussian PSF model induced ambiguity; Inter pixel interference effect; Remote sensing process; Sensitivity variation; Sensor point spread function (PSF); SPOT HRV data; Sub pixel proportion estimates

Indexed keywords

OPTICAL TRANSFER FUNCTION; PIXELS; SENSITIVITY ANALYSIS;

CONDITIONAL DISTRIBUTION; INTERFERENCE EFFECTS; PSF MODEL; SENSITIVITY VARIATION; SPOT-HRV; STATISTICAL TECHNIQUES; SUB PIXELS;

REMOTE SENSING;

EID: 0142222641     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.1002/0470035269.ch4     Document Type: Chapter

References (15)

1. Bishop, C. M., 1994, Mixture Density Networks, Technical Report NCRG/94/004. Neural Computing Research Group, Aston University.
2. Bishop, C. M., 1995, Neural Networks for Pattern Recognition (Oxford: Oxford University Press).
3. Cracknell, A. P., 1998, Synergy in remote sensing - what's in a pixel? International Journal of Remote Sensing, 19, 2025-47.
4. DeGroot, M. H., 1989, Probability and Statistics (Reading, MA: Addison-Wesley).
5. Fisher, P., 1997, The pixel: a snare and a delusion, International Journal of Remote Sensing, 18, 679-85.
6. Foody, G. M., 1995, Fully fuzzy supervised image classification, Proceedings of the 21st Annual Conference of the Remote Sensing Society (Nottingham: Remote Sensing Society), pp. 1187-94.
7. Foody, G. M., 1997, Land cover mapping from remotely sensed data with a neural network: accommodating fuzziness, in I. Kanellopoulos, G. G. Wilkinson, F. Roli and J. Austin (eds), Neural-computation in Remote Sensing Data Analysis (Berlin: Springer-Verlag), pp. 28-37.
8. Horwitz, H. M., Nalepka, R. F., Hyde, P. S. and Morgenstern, J. P., 1971, Estimating the proportions of objects within a single resolution element of a multispectral scanner, Proceedings 7th International Symposium on Remote Sensing of Environment (Michigan: Ann Arbor), pp. 1307-20.
9. Justice, C. O., Markham, B. L., Townshend, J. R. G. and Kennard, R. L., 1989, Spatial degradation of satellite data, International Journal of Remote Sensing, 10, 1539-61.
10. Kitamoto, A. and Takagi, M., 1999, Image classification using probabilistic models that reflect and internal structure of mixels, Pattern Analysis and Applications, 2, 31-43.
11. Manslow, J. F., 2001, On the Extraction and Representation of Land Cover Information Derived from Remotely Sensed Imagery, Unpublished Ph D Thesis (Southampton: University of Southampton).
12. Manslow, J. F. and Nixon, M., 2000. On the representation of fuzzy land cover classifications, Proceedings of the 26th Annual Conference of the Remote Sensing Society (Nottingham: Remote Sensing Society).
13. Manslow, J. F., Brown, J. and Nixon, M., 2000, On the probabilistic interpretation of area based fuzzy land cover mixing proportions, in S. Lek and J. F. GueÂgan (eds), Artificial Neuronal Networks: Application to Ecology and Evolution (Berlin: Springer-Verlag), pp. 81-95.
14. Townshend, J. R. G., Huang, C., Kalluri, S. N. V., DeFries, R. S. and Liang, S., 2000, Beware of per-pixel characterisation of land cover, International Journal of Remote Sensing, 21, 839-43.
15. Wilkinson, G. G., 1996, Classification algorithms - where next? in E. Binaghi, P. A. Brivio and A. Rampini (eds), Soft Computing in Remote Sensing Data Analysis (Singapore: World Scientific), pp. 93-9.