An evaluation of some factors affecting the accuracy of classification by an artificial neural network

International Journal of Remote Sensing

Volumn 18, Issue 4, 1997, Pages 799-810

  Foody, G M ^a   Arora, M K ^b  

^a UNIVERSITY OF SALFORD   (United Kingdom)

^b SWANSEA UNIVERSITY   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

IMAGE PROCESSING; MATHEMATICAL MODELS; NEURAL NETWORKS; REMOTE SENSING;

AIRBORNE THEMATIC MAPPER; CLASSIFICATION ACCURACY; LOG LINEAR MODEL;

PATTERN RECOGNITION;

CLASSIFICATION; IMAGE ANALYSIS; IMAGE LAYER; NETWORK ARCHITECTURE; NEURAL NETWORK; REMOTE SENSING; TRAINING;

EID: 0031105722     PISSN: 01431161     EISSN: 13665901     Source Type: Journal    
DOI: 10.1080/014311697218764     Document Type: Article

References (44)

1. Ahmad, S., and 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, Calif.: Morgan Kaufmann Publishers), pp. 160-168.
2. A leksander, I., and M orton, H., 1990, An Introduction to Neural Computing (London: Chapman and Hall).
3. Arora, M. Kand Foody, G. M 1997, Log-linear modelling for the evaluation of variables affecting the accuracy of probabilistic, fuzzy, and neural network classifications, International Journal of Remote Sensing, 18, 785-798 (this issue).
4. Battiti, R., 1994, Using mutual information for selecting features in supervised neural net learning. I.E.E.E. Transactions on Neural Networks, 5, 537-550.
5. Baum, E. B., and HaussLer, D., 1989, What size net gives valid generalization? In Advances in Neural Information Processing Systems I, edited by D. S. Touretzky (San Mateo, Calif.: Morgan Kaufmann Publishers), pp. 81-90.
6. Benediktsson, J. A., Swain, P. H., and Ersoy, O. K., 1990, Neural network approaches versus statistical methods in classification of multisource remote sensing data. I.E.E.E. Transactions on Geoscience and Remote Sensing, 28, 540-551.
7. Campbell, J. B., 1981, Spatial correlation effects upon accuracy of supervised classification of land cover. Photogrammetric Engineering and Remote Sensing, 47, 355-363.
8. Campbell, J. B., 1987, Introduction to Remote Sensing, 1st edn (New York: Guilford).
9. CongaLton, R. G., 1988, A comparison of sampling schemes used in generating errormatrices for assessing the accuracy of maps generated from remotely sensed data. Photogrammetric Engineering and Remote Sensing, 54, 593-600.
10. CongaLton, R. G., Heinen, J. T., and OderwaLd, R. G., 1983, Update and review of accuracy assessment techniques for remotely sensed data, final report, NASA 1.26:171-703.
11. DavaLo, E., and Naim, P., 1991, Neural Networks (Basingstoke: Macmillan).
12. Dicks, S. E., and Lo, T. H. C., 1990, Evaluation of thematic map accuracy in a land-use and land-cover mapping program. Photogrammetric Engineering and Remote Sensing, 56, 1247-1252.
13. FaHLman, S. E., 1988, Faster-learning variations of back-propagation: an empirical study. Proceedings of the 1988 Connectionist Models Summer School (San Mateo, Calif.: Morgan Kaufmann Publishers), pp. 38-51.
14. Fitzpatrick- Lins, K., 1981, Comparison of sampling procedures and data analysis for a land use and land cover map. Photogrammetric Engineering and Remote Sensing, 47, 343-51.
15. Foody, G. M., 1992, On the compensation for chance agreement in image classification accuracy assessment. Photogrammetric Engineering and Remote Sensing, 58, 1459-1460.
16. Foody, G. M., McCulloch, M. B., and Yates, W. B., 1995, The effect of training set size and composition on artificial neural network classification. International Journal of Remote Sensing, 16, 1707-1723.
17. Gong, P., and Howart H, P. J., 1990, An assessment of some factors influencing multispectral land cover classification. Photogrammetric Engineering and Remote Sensing, 56, 597-603.
18. Hepner, G. F., Logan, T., Ritter, N., and Bryant, N., 1990, Artificial neural network classification a minimal training set: comparison to conventional supervised classification. Photogrammetric Engineering and Remote Sensing, 56, 469-473.
19. Janssen, L. L. F., and van der Wel, F. J. M., 1994, Accuracy assessment of satellite derived land-cover data: a review. Photogrammetric Engineering and Remote Sensing, 60, 419-426.
20. KaLkHan, M. A., Reich, R. M., and Czaplewski, R. L., 1995, Statistical properties of five indices in assessing the accuracy of remotely sensed data using simple random sampling. Proceedings 1995 A CSM/ASPRS Annual Convention and Exposition, 2 (Bethesda, MD: American Congress on Surveying and Mapping and American Society for Photogrammetry and Remote Sensing), pp. 246-257.
21. KaneLLopouLos, I., VarFis, A., WiLkinson, G. C., 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.
22. Kenk, E., SondHeim, M., and Yee, B., 1988, Methods for improving accuracy of thematic mapper ground cover classifications. Canadian Journal of Remote Sensing, 14, 17-31.
23. Labovitz, M. L., 1986, Issues arising from sampling designs and band selection in discriminating ground reference attributes using remotely sensed data. Photogrammetric Engineering and Remote Sensing, 52, 201-211.
24. Lee, J. J., SHim, J. C., and Ha, Y. H., 1994, Stereo correspondence using the Hopfield neural network of a new energy function. Pattern Recognition, 27, 1513-1522.
25. Loung, G., and Tan, Z., 1992, Stereo matching using artificial neural networks. International Archives of Photogrammetry and Remote Sensing, 29 (B3), 417-421.
26. Lynn, D. W., White, S. J., and Morrison, A. J., 1986, Overview of the 1986 NERC airborne remote sensing campaign. Proceedings of the NERC 1986 Airborne Campaign Workshop (Swindon: Natural Environment Research Council) pp. 1-5.
27. Mather, P. M., 1987, Computer Processing of Remotely Sensed Images (Chichester: Wiley).
28. Manry, M. T., Dawson, M. S., Fung, A. K., Apollo, S. J., ALLen, L. S., and LyLe, W. D., 1994, Fast training of neural networks for remote sensing. Remote Sensing Reviews, 9, 77-96.
29. NeLson, R. F., Latty, R. S., and Mott, G., 1984, Classifying Northern forests using Thematic Mapper simulator data. Photogrammetric Engineering and Remote Sensing, 50, 607-617.
30. PeddLe, D. R., Foody, G. M., Zhang, A., FrankLin, S. E., and LeDrew, E. F., 1994, Multisource image classification II: an empirical comparison of evidential reasoning and neural network approaches. Canadian Journal of Remote Sensing, 20, 396-407.
31. 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.
32. Piper, J., 1992, Variability and bias in experimentally measured classifier error rates. Pattern Recognition Letters, 13, 685-692.
33. Richards, J. A., 1993, Remote Sensing Digital Image Analysis: An Introduction, 2nd edn (Berlin: Springer-Verlag).
34. RosenfieLd, G. H., and Fitzpatrick- Lins, K., 1986, A coefficient of agreement as a measure of thematic classification accuracy. Photogrammetric Engineering and Remote Sensing, 52, 223-227.
35. RumeLhart, D. E., Hinton, G. E., and WiLLiams, R. J., 1986, Learning internal representation by error propagation. In Parallel Distributed Processing: Explorations in the Microstructure of Cognition, edited by D. E. Rumelhart and J. L. McClelland, (Cambridge, Mass.: MIT Press), pp. 318-362.
36. SchaLkoff, R. J., 1992, Pattern Recognition: Statistical, Structural and Neural Approaches (New York: Wiley).
37. ShahshahANi, B. M., and Landgrebe, D. A., 1994, The effect of unlabelled samples in reducing the small sample size problem and mitigating the Hughes phenomenon. I.E.E.E. Transactions on Geoscience and Remote Sensing, 32, 1087 1095.
38. SheffieLd, C., 1985, Selecting band combinations from multispectral data. Photogrammetric Engineering and Remote Sensing, 51, 681-687.
39. Thomas, I. L., Benning, V. M., and Ching, N. P., 1987, Classification of Remotely Sensed Images (Bristol: Adam Hilger).
40. Townshend, J. R. G., 1984, Agricultural land-cover discrimination using thematic mapper spectral bands. International Journal of Remote Sensing, 6, 681-698.
41. Wang, Y., and Civco, D. L., 1995, Artificial neural networks in high dimensional spatial data classification: a performance evaluation, Proceedings of the 1995 ACSM/ASPRS Annual Convention and Exposition, 3 (Bethesda MD: American Congress on Surveying and Mapping and American Society for Photogrammetry and Remote Sensing) pp. 662-671.
42. WaLker, N. P., EgLen, S. J., and Lawrence, B. A., 1994, Image compression using neural networks. GEC Journal of Research, 11, 66-75.
43. WrigLey, N., 1985, Categorical Data Analysis for Geographers and Environmental Scientists (London: Longman).
44. Zhuang, X., Engel, B. A., Lozano-Garcia, D. F., Fernandez, R. N., and Johannsen, C. J., 1994, Optimization of training data required for neuro-classification. International Journal of Remote Sensing, 15, 3271-3277.