84
2: Raghuveer M. Rao, Manoj K. Arora
Foody GM (1995b) Using prior knowledge in artificial neural network classification with
a minimal training set. International Journal of Remote Sensing 16(2): 301-312
FoodyGM (1998) Issues in training set selection and refinement for classification by a feedforward neural network. Proceedings of the IEEE International Geoscience and Remote
Sensing Symposium 1, pp 409-411
Foody GM, Arora MK (1996) Incorporating mixed pixels in the training, allocation and
testing stages of supervised classifications. Pattern Recognition Letters 17(13): 13891398
Gersho A, Gray RM (1992) Vector quantization and signal compression. Kluwer Academic,
Boston, MA
Gong P, Miller JR, Spanner M (1993) Forest canopy closure from classification and spectral
mixing of scene components: multi-sensor evaluation of application to an open canopy.
Proceedings of the IEEE International Geoscience and Remote Sensing Symposium 2,
pp 747-749
Gonzalez RC, Woods RE (1992) Digital image processing. Addison-Wesley Publishing Company, Reading, Massachusetts
Gupta RP (2003) Remote sensing geology. Springer Verlag, Heidelberg
Hansen M, DeFries R, Townshend J, Sohlberg R (2000) Global land cover classification at
lkm spatial resolution using a classification tree approach. International Journal of
Remote Sensing. 21(6&7): 1331-1364
Heermann PD, Khazenie N (1992) Classification of multispectral remote sensing data using
a back-propagation neural network. IEEE Transactions on Geoscience and Remote
Sensing 30(1): 81-88
Huang X, Jensen JR (1997) A machine learning approach to automated construction of
knowledge bases for image analysis expert systems that incorporate GIS data. Photogrammetric Engineering and Remote Sensing 63(10): 1185-1194
Jensen, JR (1996) Introductory digital image processing: a remote sensing perspective.
Prentice-Hall, Upper Saddle River, NJ
Kavzoglu T, Mather PM (2003) The use of backpropagating artificial neural networks in
land cover classification. International Journal of Remote Sensing 24(23): 4907-4938
Kimes DS (1991) Inferring vegetation characteristics using a knowledge-based system.
Proceedings of the IEEE International Geoscience and Remote Sensing Symposium 4,
pp 2299-2302
Lambin EF, Strahler AH (1994) Indicators ofland-cover change for change-vector analysis
in multitemporal space at coarse spatial scales. International Journal of Remote Sensing
15: 2099-2119
Lark RM (1995) Contribution of principal components to discrimination of classes ofland
cover in multispectral imagery. International Journal of Remote Sensing 6: 779-784
Liu X, Lathrop RG (2002) Urban change detection based on an artificial neural network.
International Journal of Remote Sensing 23: 2513-2518
Ma Z, Redmond RL (1995) Tau coefficient for accuracy assessment of classification of remote
sensing data. Photogrammetric Engineering and Remote Sensing 61: 435-439
Mas JF (1999) Monitoring land-cover changes: a comparison of change detection techniques.
International Journal of Remote Sensing 20: 139-152
Maselli F, Conese C, Petkov L (1996) Use of probability entropy for the estimation and
graphical representation of the accuracy of maximum likelihood classifications. ISPRS
Journal of Photog ram me try and Remote Sensing 49(2): 13-20
Mather PM (1999) Computer processing of remotely-sensed images: an introduction. Wiley,
Chichester
2: Raghuveer M. Rao, Manoj K. Arora
Foody GM (1995b) Using prior knowledge in artificial neural network classification with
a minimal training set. International Journal of Remote Sensing 16(2): 301-312
FoodyGM (1998) Issues in training set selection and refinement for classification by a feedforward neural network. Proceedings of the IEEE International Geoscience and Remote
Sensing Symposium 1, pp 409-411
Foody GM, Arora MK (1996) Incorporating mixed pixels in the training, allocation and
testing stages of supervised classifications. Pattern Recognition Letters 17(13): 13891398
Gersho A, Gray RM (1992) Vector quantization and signal compression. Kluwer Academic,
Boston, MA
Gong P, Miller JR, Spanner M (1993) Forest canopy closure from classification and spectral
mixing of scene components: multi-sensor evaluation of application to an open canopy.
Proceedings of the IEEE International Geoscience and Remote Sensing Symposium 2,
pp 747-749
Gonzalez RC, Woods RE (1992) Digital image processing. Addison-Wesley Publishing Company, Reading, Massachusetts
Gupta RP (2003) Remote sensing geology. Springer Verlag, Heidelberg
Hansen M, DeFries R, Townshend J, Sohlberg R (2000) Global land cover classification at
lkm spatial resolution using a classification tree approach. International Journal of
Remote Sensing. 21(6&7): 1331-1364
Heermann PD, Khazenie N (1992) Classification of multispectral remote sensing data using
a back-propagation neural network. IEEE Transactions on Geoscience and Remote
Sensing 30(1): 81-88
Huang X, Jensen JR (1997) A machine learning approach to automated construction of
knowledge bases for image analysis expert systems that incorporate GIS data. Photogrammetric Engineering and Remote Sensing 63(10): 1185-1194
Jensen, JR (1996) Introductory digital image processing: a remote sensing perspective.
Prentice-Hall, Upper Saddle River, NJ
Kavzoglu T, Mather PM (2003) The use of backpropagating artificial neural networks in
land cover classification. International Journal of Remote Sensing 24(23): 4907-4938
Kimes DS (1991) Inferring vegetation characteristics using a knowledge-based system.
Proceedings of the IEEE International Geoscience and Remote Sensing Symposium 4,
pp 2299-2302
Lambin EF, Strahler AH (1994) Indicators ofland-cover change for change-vector analysis
in multitemporal space at coarse spatial scales. International Journal of Remote Sensing
15: 2099-2119
Lark RM (1995) Contribution of principal components to discrimination of classes ofland
cover in multispectral imagery. International Journal of Remote Sensing 6: 779-784
Liu X, Lathrop RG (2002) Urban change detection based on an artificial neural network.
International Journal of Remote Sensing 23: 2513-2518
Ma Z, Redmond RL (1995) Tau coefficient for accuracy assessment of classification of remote
sensing data. Photogrammetric Engineering and Remote Sensing 61: 435-439
Mas JF (1999) Monitoring land-cover changes: a comparison of change detection techniques.
International Journal of Remote Sensing 20: 139-152
Maselli F, Conese C, Petkov L (1996) Use of probability entropy for the estimation and
graphical representation of the accuracy of maximum likelihood classifications. ISPRS
Journal of Photog ram me try and Remote Sensing 49(2): 13-20
Mather PM (1999) Computer processing of remotely-sensed images: an introduction. Wiley,
Chichester