The use of backpropagating artificial neural networks in land cover classification

References

• Abuelgasim , A. A , Gopal , S , Irons , J. R and Strahler , A. H . 1996 . Classification of ASAS multiangle and multispectral measurements using artificial neural networks . Remote Sensing of Environment , 57 : 79 – 87 .
   Web of Science ®Google Scholar
• Ahmad S Tesauro G 1989 Scaling and generalization in neural networks: a case study In Advances in Neural Information Processing Systems I, edited by D. S. Touretzky (San Mateo: Morgan Kaufmann), pp. 160–168
   Google Scholar
• Ardö , J , Pilesjö , P and Skidmore , A . 1997 . Neural networks, multitemporal Landsat Thematic Mapper data and topographic data to classify forest damages in the Czech Republic . Canadian Journal of Remote Sensing , 23 : 217 – 229 .
   Google Scholar
• Atkinson , P. M and Tatnall , A. R. L . 1997 . Neural networks in remote sensing . International Journal of Remote Sensing , 18 : 699 – 709 .
   Web of Science ®Google Scholar
• Baum E. B Haussler D 1989 What size net gives valid generalization? In Advances in Neural Information Processing Systems I, edited by D. S. Touretzky (San Mateo: Morgan Kaufmann), pp. 81–90
   Google Scholar
• Bebis , G , Georgiopoulos , M and Kasparis , T . 1997 . Coupling weight elimination with genetic algorithms to reduce network size and preserve generalization . Neurocomputing , 17 : 167 – 194 .
   Web of Science ®Google Scholar
• Benediktsson , J. A , Swain , P. H and Ersoy , O. K . 1990 . Neural network approaches versus statistical methods in classification of multisource remote sensing data . IEEE Transactions on Geoscience and Remote Sensing , 28 : 540 – 551 .
   Web of Science ®Google Scholar
• Bischof , H , Schneider , W and Pinz , A. J . 1992 . Multispectral classification of Landsat-images using neural networks . IEEE Transactions on Geoscience and Remote Sensing , 30 : 482 – 489 .
   Web of Science ®Google Scholar
• Blamire P. A 1994 An investigation into the identification and classification of urban areas from remotely sensed satellite data using neural networks. MSc Thesis, Edinburgh University, UK
   Google Scholar
• Blamire , P. A . 1996 . The influence of relative sample size in training artificial neural networks . International Journal of Remote Sensing , 17 : 223 – 230 .
   Web of Science ®Google Scholar
• Blamire P. A Mineter M. J 1995 On the identification of urban areas from Landsat TM data using artificial neural networks Proceedings of the 21st Annual Conference of the Remote Sensing Society, Southampton, UK, 11–14 September 1995, (Nottingham: Remote Sensing Society), pp. 50–57
   Google Scholar
• Bruzzone , L , Conese , C , Maselli , F and Roli , F . 1997 . Multisource classification of complex rural areas by statistical and neural-network approaches . Photogrammetric Engineering and Remote Sensing , 63 : 523 – 533 .
   Web of Science ®Google Scholar
• Chester D. L 1990 Why two hidden layers are better than one Proceedings of the International Joint Conference on Neural Network, Washington, USA, January 1990, edited by M. Caudill (Hillsdale, NJ: Lawrence Erlbaum), pp. 265–268
   Google Scholar
• Cybenko , G . 1989 . Approximation by superpositions of a sigmoidal function . Mathematics of Control, Signals, and Systems , 2 : 303 – 314 .
   Google Scholar
• Eaton , H. A. C and Olivier , T. L . 1992 . Learning coefficient dependence on training set size . Neural Networks , 5 : 283 – 288 .
   Web of Science ®Google Scholar
• Eberhart R. C Dobbins R. W 1990 Neural Network PC Tools – A Practical Guide (London: Academic Press)
   Google Scholar
• Fitch , J. P , Lehman , S. K , Dowla , F. U , Lu , S. Y , Johansson , E. M and Goodman , D. M . 1991 . Ship wake-detection procedure using conjugate gradient trained artificial neural networks . IEEE Transactions on Geoscience and Remote Sensing , 29 : 718 – 726 .
   Web of Science ®Google Scholar
• Foody , G. M . 1995 . Using prior knowledge in artificial neural network classification with a minimal training set . International Journal of Remote Sensing , 16 : 301 – 312 .
   Web of Science ®Google Scholar
• Foody , G. M , Lucas , R. M , Curran , P. J and Honzak , M . 1996 . Estimation of the areal extent of land cover classes that only occur at a sub-pixel level . Canadian Journal of Remote Sensing , 22 : 428 – 432 .
   Google Scholar
• Fu L. M 1994 Neural Networks in Computer Intelligence (London: McGraw-Hill)
   Google Scholar
• Gallagher M Downs T 1997 Visualisation of learning in neural networks using principal component analysis Proceedings of International Conference on Computational Intelligence and Multimedia Applications, Gold Coast, Australia, 10–12 February 1997, edited by B. Verma and X. Yao, pp. 327–331
   Google Scholar
• Garson G. D 1998 Neural Networks: An Introductory Guide for Social Scientists (London: Sage)
   Google Scholar
• Gong , P . 1996 . Integrated analysis of spatial data from multiple sources: using evidential reasoning and artificial neural network techniques for geological mapping . Photogrammetric Engineering and Remote Sensing , 62 : 513 – 523 .
   Web of Science ®Google Scholar
• Gong , P , Pu , R and Chen , J . 1996 . Mapping ecological land systems and classification uncertainties from digital elevation and forest-cover data using neural networks . Photogrammetric Engineering and Remote Sensing , 62 : 1249 – 1260 .
   Web of Science ®Google Scholar
• Gopal , S and Woodcock , C . 1996 . Remote sensing of forest change using artificial neural networks . IEEE Transactions on Geoscience and Remote Sensing , 34 : 398 – 403 .
   Web of Science ®Google Scholar
• Hand D. J 1997 Construction and Assessment of Classification Rules (New York: John Wiley)
   Google Scholar
• Hara , Y , Atkins , R. G , Yueh , S. H , Shin , R. T and Kong , J. A . 1994 . Application of neural networks to radar image classification . IEEE Transactions on Geoscience and Remote Sensing , 32 : 100 – 109 .
   Web of Science ®Google Scholar
• Haykin S 1999 Neural Networks: A Comprehensive Foundation (New Jersey: Prentice Hall)
   Google Scholar
• Hecht-Nielsen R 1987 Kolmogorov's mapping neural network existence theorem Proceedings of the First IEEE International Conference on Neural Networks, San Diego, CA, 21–24 June 1987, edited by M. Caudill and C. Butler (New York: IEEE), pp. 11–14
   Google Scholar
• Heermann , P. D and Khazenie , N . 1992 . Classification of multispectral remote sensing data using a back-propagation neural network . IEEE Transactions on Geoscience and Remote Sensing , 30 : 81 – 88 .
   Web of Science ®Google Scholar
• Hepner , G. F , Logan , T , Ritter , N and Bryant , N . 1990 . Artificial neural network classification using a minimal training set: comparison to conventional supervised classification . Photogrammetric Engineering and Remote Sensing , 56 : 469 – 473 .
   Web of Science ®Google Scholar
• Huang , S. C and Huang , Y. F . 1991 . Bounds on the number of hidden neurons in multilayer perceptrons . IEEE Transactions on Neural Networks , 2 : 47 – 55 .
   PubMed Web of Science ®Google Scholar
• Hush D. R 1989 Classification with neural networks: a performance analysis Proceedings of the IEEE International Conference on Systems Engineering, Dayton, Ohio, USA, pp. 277–280
   Google Scholar
• Hush D. R Horne B. G 1993 Progress in supervised neural networks IEEE Signal Processing Magazine, January, 8–39
   Google Scholar
• Hush , D. R , Horne , B. G and Salas , J. M . 1992 . Error surfaces for multilayer perceptrons . IEEE Transactions on Systems, Man, and Cybernetics , 22 : 1152 – 1161 .
   Web of Science ®Google Scholar
• Huurneman , G , Gens , R and Broekema , L . 1996 . Thematic information extraction in a neural network classification of multi-sensor data including microwave phase information . International Archives of Photogrammetry and Remote Sensing , XXXI : 170 – 175 .
   Google Scholar
• Kanellopoulos , I and Wilkinson , G. G . 1997 . Strategies and best practice for neural network image classification . International Journal of Remote Sensing , 18 : 711 – 725 .
   Web of Science ®Google Scholar
• Kanellopoulos , I , Varfis , A , Wilkinson , G. G and Megier , J . 1992 . Land-cover discrimination in SPOT HRV imagery using an artificial neural network – a 20-class experiment . International Journal of Remote Sensing , 13 : 917 – 924 .
   Web of Science ®Google Scholar
• Kavzoglu T 2001 An investigation of the design and use of feed-forward artificial neural networks in the classification of remotely sensed images, PhD Thesis, Nottingham University, UK
   Google Scholar
• Kavzoglu , T and Mather , P. M . 1999 . Pruning artificial neural networks: an example using land cover classification of multi-sensor images . International Journal of Remote Sensing , 20 : 2787 – 2803 .
   Web of Science ®Google Scholar
• Kavzoglu , T and Mather , P. M . 2002 . The role of feature selection in artificial neural network applications . International Journal of Remote Sensing , 23 : 2919 – 2937 .
   Web of Science ®Google Scholar
• Klimasauskas C. C 1993 Applying neural networks In Neural Networks in Finance and Investing, edited by R. R. Trippi and E. Turban (Cambridge: Probus), pp. 47–72
   Google Scholar
• Kolen , J. F and Pollack , J. B . 1990 . Back propagation is sensitive to initial conditions . Complex Systems , 4 : 269 – 280 .
   Google Scholar
• Lawrence S Giles C. L Tsoi A. C 1996 What size neural network gives optimal generalization? Convergence properties of backpropagation UMIA CS-TR-96-22 and CS-TR-3617, Institute for Advanced Computer Studies, University of Maryland, Maryland, USA
   Google Scholar
• Lippmann , R. P . 1987 . An introduction to computing with neural nets . IEEE ASSP Magazine , April : 4 – 22 .
   Google Scholar
• Mather P. M 1999 Computer Processing of Remotely-Sensed Images: An introduction (Chichester: John Wiley)
   Google Scholar
• Messer K Kittler J 1998 Choosing an optimal neural network size to aid search through a large image database Proceedings of the 9th British Machine Vision Conference, University of Southampton, UK, 14–17 September 1998, edited by J. N. Carter and M. S. Nixon (London: British Machine Vision Association), pp. 235–244
   Google Scholar
• Moreira M Fiesler E 1995 Neural networks with adaptive learning rate and momentum term IDIAP Technical Report: 95–04, (ftp://ftp.idiap.ch/pub/techreports/95–04.ps.Z)
   Google Scholar
• Paola J. D 1994 Neural network classification of multispectral imagery. MSc Thesis, The University of Arizona, Tucson, USA
   Google Scholar
• Paola J. D Schowengerdt R. A 1994 Comparisons of neural networks to standard techniques for image classification and correlation Proceedings of the International Geoscience and Remote Sensing Symposium, Pasadena, CA, 8–12 August 1994 (New York: IEEE), pp. 1404–1406
   Google Scholar
• Paola , J. D and Schowengerdt , R. A . 1995a . A detailed comparison of backpropagation neural network and maximum-likelihood classifiers for urban land use classification . IEEE Transactions on Geoscience and Remote Sensing , 33 : 981 – 996 .
   Web of Science ®Google Scholar
• Paola , J. D and Schowengerdt , R. A . 1995b . A review and analysis of backpropagation neural networks for classification of remotely-sensed multi-spectral imagery . International Journal of Remote Sensing , 16 : 3033 – 3058 .
   Web of Science ®Google Scholar
• Paola , J. D and Schowengerdt , R. A . 1997 . The effect of neural-network structure on a multispectral land-use/land-cover classification . Photogrammetric Engineering and Remote Sensing , 63 : 535 – 544 .
   Web of Science ®Google Scholar
• Partridge , D and Yates , W. B . 1996 . Replicability of neural computing experiments . Complex Systems , 10 : 257 – 281 .
   Google Scholar
• Pierce , L. E , Sarabandi , K and Ulaby , F. T . 1994 . Application of an artificial neural network in canopy scattering inversion . International Journal of Remote Sensing , 15 : 3263 – 3270 .
   Web of Science ®Google Scholar
• Richard , M. D and Lippmann , R. R . 1991 . Neural network classifiers estimate Bayesian a posteriori probabilities . Neural Computation , 3 : 461 – 483 .
   PubMed Web of Science ®Google Scholar
• Ripley B. D 1993 Statistical aspects of neural networks In Networks and Chaos – Statistical and Probabilistic Aspects, edited by O. E. Barndorff-Nielsen, J. L. Jensen, and W. S. Kendall (London: Chapman & Hall), pp. 40–123
   Google Scholar
• Rumelhart D. E McClelland J. L PDP Group 1986 Parallel Distributed Processing: Explorations in the Microstructure of Cognition: Foundations (London: The MIT Press)
   Google Scholar
• Sarle W. S 1995 Stopped training and other remedies for overfitting Proceedings of the 27th Symposium on the Interface of Computing Science and Statistics, Cary, NC, 21–24 June 1995 (Fairfax Station, VA: Interface Foundation of North America), pp. 352–360
   Google Scholar
• Sarle W. S 2000 Neural network FAQ (ftp://ftp.sas.com/pub/neural/FAQ.html)
   Google Scholar
• Sietsma , J and Dow , R. J. F . 1991 . Creating artificial neural networks that generalize . Neural Networks , 4 : 67 – 79 .
   Web of Science ®Google Scholar
• Skidmore , A. K , Turner , B. J , Brinkhof , W and Knowles , E . 1997 . Performance of a neural network: mapping forests using GIS and remotely sensed data . Photogrammetric Engineering and Remote Sensing , 63 : 501 – 514 .
   Web of Science ®Google Scholar
• Sontag , E. D . 1989 . Sigmoids distinguish better than Heavisides . Neural Computation , 1 : 470 – 472 .
   Web of Science ®Google Scholar
• Sontag E. D 1998 VC dimension of neural networks In Neural Networks and Machine Learning, edited by C. M. Bishop (Berlin: Springer-Verlag), pp. 69–95
   Google Scholar
• Staufer P Fischer M. M 1997 Spectral pattern recognition by a two-layer perceptron: effects of training set size In Neurocomputation in Remote Sensing Data Analysis, edited by I. Kanellopoulos, G. G. Wilkinson, F. Roli, and J. Austin (London: Springer), pp. 105–116
   Google Scholar
• Swingler K 1996 Applying Neural Networks – A Practical Guide (London: Academic Press)
   Google Scholar
• Thackrah G Barnsley M Pan P 1999 Merging land cover classifications from artifical neural networks using the Sugeno fuzzy integral Proceedings of the 25th Annual Conference and Exhibition of the Remote Sensing Society, Cardiff, UK, 8–10 September 1999 (Nottingham: Remote Sensing Society), pp. 71–78
   Google Scholar
• Vuurpijl L 1998 Platforms for artificial neural networks. PhD Thesis, The University of Nijmegen, The Netherlands
   Google Scholar
• Wang C 1994a A theory of generalisation in learning machines with neural network application, PhD Thesis, The University of Pennsylvania, Philadelphia, USA
   Google Scholar
• Wang , F . 1994b . The use of artificial neural networks in a geographical information system for agricultural land-suitability assessment . Environment and Planning A , 26 : 265 – 284 .
   Web of Science ®Google Scholar