Chapter 20 . Classification of Remotely Sensed Images
397
Paola JD, Schowengerdt RA (1995) A detailed comparison of backpropagation neural
network and maximum likelihood classification for urban land use classification, IEEE
Transactions on Geoscience and Remote Sensing, 33, 981-996
Peddle DR, Foody GM, Zhang A, FrankIin SE, LeDrew EF (1994) Multisource image
classification II: an empirical comparison of evidential reasoning and neural network
approaches, Canadian Journal of Remote Sensing, 20, 396-407
Poth A, Klaus M, Stein G (2001) Optimisation at multi-spectral land cover classification
with fuzzy clustering and the Kohonen feature map, International Journal of Remote
Sensing, 22, 1423-1439
Rollet R, Benie GB, Li W, Wang S, Boucher JM (1998) Image classification algorithm
based on the RBF neural network and K-means, International Journal of Remote
Sensing, 19,3003-3009
Roughgarden J, Running SW, Matson PA (1991) What does remote sensing for ecology?
Ecology, 72, 1918-1922
Scepan J (1999) Thematic validation of high-resolution global land-cover data sets,
Photogrammetric Engineering and Remote Sensing, 65, 1051-1060
Schalkoff R (1992) Pattern Recognition: Statistical, Structural and Neural Approaches
Wiley, New York
Schowengerdt RA (1997) Remote Sensing: Models and Methods for Image Processing,
second edition, Academic Press, San Diego
Shahshahani BM, Landgrebe DA (1994) The effect of unlabeled sampies in reducing the
small sample-size problem and mitigating the Hughes phenomenon, IEEE
Transactions on Geoscience and Remote Sensing, 32, 1087-1095
Smith JA (1993) LAI inversion using a back-propagating neural network trained with a
multiple scattering model, IEEE Transactions on Geoscience and Remote Sensing, 31,
1102-1106
Strahler AH (1980) The use of prior probabilities in maximum likelihood classification of
remotely sensed data, Remote Sensing of Environment, 10, 135-163
Tatem AJ, Lewis HG, Atkinson PM, Nixon MS (2001) Super-resolution target
identification from remotely sensed images using a Hopfield neural network, IEEE
Transactions on Geoscience and Remote Sensing, 39, 781-796
Townshend JRG (1992) Land cover, International Journal of Remote Sensing, 13, 13191328
Vasconcelos GC, Fairhurst MC, Bisset DL (1995) Investigating feedforward neural
networks with respect to the rejection of spurious patterns, Pattern Recognition
Letters, 16,213-218
Wang F (1994) The use of artificial neural networks in a geographical information system
for agriculturalland-suitability assessment, Environment and Planning A, 26, 265-284
Wang Y, Dong D (1997) Retrieving forest stand parameters from SAR backscatter data
using a neural network trained by a canopy backscatter model, International Journal of
Remote Sensing, 18,981-989
Wiess M, Baret F (1999) Evaluation of canopy biophysical variable retrieval performances
from the accumulation of large swath satellite data, Remote Sensing of Environment,
70,293-306
Wilkinson GG (1996) Classification algorithms - where next?, In: Binaghi, E., Brivio, P.
A., Rampini, A. (eds) Soft Computing in Remote Sensing Data Analysis World
Scientific, Singapore, pp. 93-99
397
Paola JD, Schowengerdt RA (1995) A detailed comparison of backpropagation neural
network and maximum likelihood classification for urban land use classification, IEEE
Transactions on Geoscience and Remote Sensing, 33, 981-996
Peddle DR, Foody GM, Zhang A, FrankIin SE, LeDrew EF (1994) Multisource image
classification II: an empirical comparison of evidential reasoning and neural network
approaches, Canadian Journal of Remote Sensing, 20, 396-407
Poth A, Klaus M, Stein G (2001) Optimisation at multi-spectral land cover classification
with fuzzy clustering and the Kohonen feature map, International Journal of Remote
Sensing, 22, 1423-1439
Rollet R, Benie GB, Li W, Wang S, Boucher JM (1998) Image classification algorithm
based on the RBF neural network and K-means, International Journal of Remote
Sensing, 19,3003-3009
Roughgarden J, Running SW, Matson PA (1991) What does remote sensing for ecology?
Ecology, 72, 1918-1922
Scepan J (1999) Thematic validation of high-resolution global land-cover data sets,
Photogrammetric Engineering and Remote Sensing, 65, 1051-1060
Schalkoff R (1992) Pattern Recognition: Statistical, Structural and Neural Approaches
Wiley, New York
Schowengerdt RA (1997) Remote Sensing: Models and Methods for Image Processing,
second edition, Academic Press, San Diego
Shahshahani BM, Landgrebe DA (1994) The effect of unlabeled sampies in reducing the
small sample-size problem and mitigating the Hughes phenomenon, IEEE
Transactions on Geoscience and Remote Sensing, 32, 1087-1095
Smith JA (1993) LAI inversion using a back-propagating neural network trained with a
multiple scattering model, IEEE Transactions on Geoscience and Remote Sensing, 31,
1102-1106
Strahler AH (1980) The use of prior probabilities in maximum likelihood classification of
remotely sensed data, Remote Sensing of Environment, 10, 135-163
Tatem AJ, Lewis HG, Atkinson PM, Nixon MS (2001) Super-resolution target
identification from remotely sensed images using a Hopfield neural network, IEEE
Transactions on Geoscience and Remote Sensing, 39, 781-796
Townshend JRG (1992) Land cover, International Journal of Remote Sensing, 13, 13191328
Vasconcelos GC, Fairhurst MC, Bisset DL (1995) Investigating feedforward neural
networks with respect to the rejection of spurious patterns, Pattern Recognition
Letters, 16,213-218
Wang F (1994) The use of artificial neural networks in a geographical information system
for agriculturalland-suitability assessment, Environment and Planning A, 26, 265-284
Wang Y, Dong D (1997) Retrieving forest stand parameters from SAR backscatter data
using a neural network trained by a canopy backscatter model, International Journal of
Remote Sensing, 18,981-989
Wiess M, Baret F (1999) Evaluation of canopy biophysical variable retrieval performances
from the accumulation of large swath satellite data, Remote Sensing of Environment,
70,293-306
Wilkinson GG (1996) Classification algorithms - where next?, In: Binaghi, E., Brivio, P.
A., Rampini, A. (eds) Soft Computing in Remote Sensing Data Analysis World
Scientific, Singapore, pp. 93-99