References
277
Foody GM (2000b) Estimation of sub-pixel land cover composition in the presence of
untrained classes. Computers and Geosciences 26: 469-478
Foody GM, Arora MK (1996) Incorporating mixed pixels in the training, allocation and
testing stages of supervised classifications. Pattern Recognition Letters 17(13): 13891398
Foody GM, Cox DP (1994) Sub-pixel land cover composition estimation using a linear mixture model and fuzzy membership functions. International Journal of Remote Sensing
15:619-631
Hansen M, Dubayah R, DeFries R (1996) Classification trees: an alternative to traditional
land cover classifiers. International Journal of Remote Sensing 17: 1075-1081
Ibrahim MA, Arora MK, Ghosh SK (2003) A comparison ofFCM and PCM for sub-pixel classification. Proceedings of 151 Indian International Conference on Artificial Intelligence,
Hyderabad, India. CD
Mather PM (1999) Computer processing of remotely-sensed images: an introduction. Wiley,
Chichester
Schneider W (1993) Land use mapping with subpixel accuracy from Landsat TM image
data. Proceedings of 25 th International Symposium on Remote Sensing and Global
Environmental Change, Ann Arbor, MI, pp 155-161.
Settle JJ, Drake NA (1993) Linear mixing and the estimation of ground cover proportions.
International Journal of Remote Sensing 14: 1159-1177
Tatem AJ, Hugh G, Atkinson PM, Nixon MS (2001) Super-resolution target identification
from remotely sensed images using a Hopfield neural network. IEEE Transactions on
Geosciences and Remote Sensing 39(4): 781-796
Tatem AJ, Hugh G, Atkinson PM, Nixon MS (2002) Super-resolution land cover pattern
prediction using a Hopfield neural network. Remote Sensing of Environment 79: 1-14
Van Trees HL (1968) Detection, estimation and modulation theory. Wiley, New York
Varshney PK (1997) Distributed detection and data fusion. Springer Verlag, New York
Verhoeye J, WulfRD (2002) Land cover mapping at sub-pixel scales using linear optimization
techniques. Remote Sensing of Environment 79: 96-104
Winkler G (1995) Image analysis random fields and dynamic Monte Carlo methods. Springer
Verlag, New York
277
Foody GM (2000b) Estimation of sub-pixel land cover composition in the presence of
untrained classes. Computers and Geosciences 26: 469-478
Foody GM, Arora MK (1996) Incorporating mixed pixels in the training, allocation and
testing stages of supervised classifications. Pattern Recognition Letters 17(13): 13891398
Foody GM, Cox DP (1994) Sub-pixel land cover composition estimation using a linear mixture model and fuzzy membership functions. International Journal of Remote Sensing
15:619-631
Hansen M, Dubayah R, DeFries R (1996) Classification trees: an alternative to traditional
land cover classifiers. International Journal of Remote Sensing 17: 1075-1081
Ibrahim MA, Arora MK, Ghosh SK (2003) A comparison ofFCM and PCM for sub-pixel classification. Proceedings of 151 Indian International Conference on Artificial Intelligence,
Hyderabad, India. CD
Mather PM (1999) Computer processing of remotely-sensed images: an introduction. Wiley,
Chichester
Schneider W (1993) Land use mapping with subpixel accuracy from Landsat TM image
data. Proceedings of 25 th International Symposium on Remote Sensing and Global
Environmental Change, Ann Arbor, MI, pp 155-161.
Settle JJ, Drake NA (1993) Linear mixing and the estimation of ground cover proportions.
International Journal of Remote Sensing 14: 1159-1177
Tatem AJ, Hugh G, Atkinson PM, Nixon MS (2001) Super-resolution target identification
from remotely sensed images using a Hopfield neural network. IEEE Transactions on
Geosciences and Remote Sensing 39(4): 781-796
Tatem AJ, Hugh G, Atkinson PM, Nixon MS (2002) Super-resolution land cover pattern
prediction using a Hopfield neural network. Remote Sensing of Environment 79: 1-14
Van Trees HL (1968) Detection, estimation and modulation theory. Wiley, New York
Varshney PK (1997) Distributed detection and data fusion. Springer Verlag, New York
Verhoeye J, WulfRD (2002) Land cover mapping at sub-pixel scales using linear optimization
techniques. Remote Sensing of Environment 79: 96-104
Winkler G (1995) Image analysis random fields and dynamic Monte Carlo methods. Springer
Verlag, New York