Comparison of neural networks and statistical methods in classification of ecological habitats using FIA data

Forest Science

Volumn 49, Issue 4, 2003, Pages 619-631

  Liu, Chuangmin ^a   Zhang, Lianjun ^a   Davis, Craig J ^a   Solomon, Dale S ^b   Brann, Thomas B ^c   Caldwell, Lawrence E ^d  

^a STATE UNIVERSITY OF NEW YORK   (United States)

^b USDA FOREST SERVICE   (United States)

^c UNIVERSITY OF MAINE   (United States)

^d LE Caldwell Co   (United States)

Author keywords

Accuracy assessment; K nearest neighbor classification; Linear discriminant analysis; Minimum distance classification; Multi Layer Perceptron (MLP); Radial Basis Function (RBF)

Indexed keywords

ALGORITHMS; ECOLOGY; NEURAL NETWORKS; STATISTICAL METHODS;

ECOLOGICAL HABITATS;

FORESTRY;

EID: 0042427272     PISSN: 0015749X     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (56)

1. BASTIN, L. 1997. Comparison of fuzzy c-means classification, linear mixture modeling and MLC probabilities as tools for unmixing coarse pixels. Internat. J. Rem. Sens. 18(17):3629-3648.
2. BENEDIKTSSON, J.A., P.H. SWAIN, AND O.K. ERSOY. 1990. Neural network approaches versus statistical methods in classification of multisource remote sensing data. IEEE Transactions on Geosci. Rem. Sens. 28(4):540-551.
3. BRUZZONE, L., C. CONESE, F. MASELLI, AND F. ROLI 1997. Multisource classification of complex rural areas by statistical and neural network approaches. Photogramm. Eng. Rem. Sens. 63(5):523-533.
4. CAMPBELL, W.J., S.E. HILL, AND R.F. CROMP. 1989. Automatic labeling and characterization of objects using artificial neural networks. Telemat. Informat. 6:259-271.
5. CHEN, J., P. GONG, J. NIE, AND J.A.R. BLAIS. 1993. Application of neural networks in forest ecological classification. P. 65-71 in Tech. Pap. of ASPRS/ACSM Annu. Conv. and Expos., Vol. 3.
6. CHO, T.H., R.W. CONNERS, AND P.A. ARAMAN. 1991. A comparison of rule-based, K-nearest neighbor, and neural net classifiers for automated industrial inspection. P. 202-209 in Proc. of the IEEE/ACM Internat. Conf.on Dev. and Managing Expert Syst. Prog.
7. CIVCO, D.F. 1993. Artificial neural networks for land cover classification and mapping. Internat. J. Geog. Inf. Syst. 7:173-186.
8. CONGALTON, R.G. 1991. A review of assessing the accuracy of classification of remote sensed data. Rem. Sens. Environ. 37:35-46.
9. CONGALTON, R.G., R.G. ODERWALD, AND R.A. MEAD. 1983. Assessing Landsat classification accuracy using discrete multivariate analysis statistical techniques. Photogramm. Eng. Rem. Sens. 49(12): 1671-1678.
10. CONGALTON, R.G., AND K. GREEN. 1999. Assessing the accuracy of remotely sensed data: Principles and practices. Lewis Publishers, CRC Press, Boca Raton, FL. 137 p.
11. th Annu. Conf. of the UK Rem. Sens. Soc., Cracknell, R.A., and R.A. Vaughan (eds.).
12. DUDA, R.O., AND P.E. HART. 1973. Pattern classification and scene analysis. Wiley, New York. 482 p.
13. DUDA, R.O., P.E. HART, AND D.G. STORK. 2001. Pattern classification. Ed. 2. Wiley, New York. 654 p.
14. FISCHER, M.M. 1997. Computational neural networks: a new paradigm for spatial analysis. Environ. Plan. A 29:1873-1891.
15. FLEISS, J.L. 1981. Statistical methods for rates and proportions. Ed. 2. Wiley, New York. 321 p.
16. FOODY, G.M. 1999. The continuum of classification fuzziness in thematic mapping. Photogramm. Eng. Rem. Sens. 65(4):443-451.
17. FOODY, G.M., M.B. MCCULLOCH, AND W.B. YATES. 1995. Classification of remotely sensed data by an artificial neural network: issues related to training data characteristics. Photogramm. Eng. Rem. Sens. 61(4):391-401.
18. FUKUNAGA, K. 1972. Introduction to statistical pattern recognition. Academic Press, New York. 369 p.
19. GONG, P., R. PU, AND J. CHEN. 1996. Mapping ecological land systems and classification uncertainties from digital elevation and forest cover data using neural networks. Photogramm. Eng. Rem. Sens.62(11):1249-1260.
20. GOPAL, S. AND C. WOODCOCK. 1994. Theory and methods for accuracy assessment of thematic maps using fuzzy sets. Photogramm. Eng. Rem. Sens.60:181-188.
21. GRIFFITH, D.M., AND C.L. ALERICH. 1996. Forest statistics of Maine, 1995. USDA For. Sev. Resour. Bull. NE-135. 134 p.
22. HEPNER, G.F., T. LOGAN, N. RITTER, AND N. BRYANT. 1990. Artificial neural network classification using a minimal training set: Comparison to conventional supervised classification. Photogramm. Eng. Rem. Sens.56:469-473.
23. HERTZ, J.A., A.S. KROUG, AND R.G. PALMER. 1991. Introduction to the theory of neural computation. Addison Wesley, New York. 538 p.
24. HUANG, W.Y., AND R.P. LIPPMAN. 1987. Comparisons between neural net and conventional classifiers. P. 485-493 in Proc. of IEEE First Internat. Conf. on Neural Networks. Vol. 4. Piscataway, NJ.
25. HUDSON, W.D., AND C.W. RAMM. 1987. Correct formulation of the Kappa coefficient agreement. Photogramm. Eng. Rem. Sens.53(4):421-422.
26. KENT, J.T., AND K.V. MARDIA. 1988. Spatial classification using fuzzy membership models. IEEE Transact. Pattern Recog. Mach. Intell. 10:659-671.
27. KEY, J.R., J.A. MASLANIK, AND R.G. BARRY. 1989a. Cloud classification from satellite data using a fuzzy sets algorithm: A polar example. Internat. J. Rem. Sens.10:1823-1842.
28. KEY, J.R., A.MASLANIK, AND A.J.SCHWEIGER. 1989b. Classification of merged AVHRR and SMMR arctic data with neural networks. Photogramm. Eng. Rem. Sens.55(9):1331-1338.
29. KOUKOULAS, S., AND G.A. BLACKBURN. 2001. Introducing new indices for accuracy evaluation of classified image representing semi-natural woodland environments. Photogramm. Eng. Rem. Sens. 67(4):449-510.
30. KUKOLOCH, L., AND R. LIPPMANN. 1999. LNKnet User's Guide. MIT Lincoln Laboratory. (The software and the manual can be downloaded at http://www.ll.mit.edu/IST).
31. LEAK, W.B. 1982. Habitat mapping and interpretation in New England. USDA For. Serv. Res. Pap. NE-496. 28 p.
32. LEK, S., AND J.F. GUEGAN. 1999. Artificial neural networks as a tool in ecological modeling: An introduction. Ecol. Model. 120:65-73.
33. LIPPMANN, R.P. 1987. An introduction to computing with neural nets. IEEE ASSP Mag. April: 4-22.
34. MCCLELLAND, G.E., R.N. DEWITT, T.H. HEMMER, L.N. MATHESON, AND G.O. MOE. 1989. Multispectral image-processing with a three-layer back-propagation network. Proc. Internat. Joint Conf. on Neural Networks 1:151-153.
35. MITRA, S., AND J. BASAK. 2001. FRBF: A fuzzy radial basis function network. Neural Comput. Appl. 10:244-252.
36. MORLINI, I. 1999. Radial basis function networks with partially classified data. Ecol. Model. 120:109-118.
37. MURNION, S. 1999. Exploring spatial non-stationarity with radial basis function neural networks.Geogr. Environ. Model. 3(1):35-45.
38. NG, K., AND R.P. LIPPMANN, 1991. A comparative study of the practical characteristics of neural network and conventional pattern classifiers. P. 970-976 in Neural Information Proc.Systems 3. R. Lippmann, R., et al. (eds.). Morgan Kaufman, San Mateo, California.
39. PAO, Y.H. 1989. Adaptive pattern recognition and neural networks. Addison-Wesley, New York. 309 p.
40. PAOLA, J.D., AND R.A. SCHOWENGERDT. 1997. The effect of neural-network structure on a multispectral land-use/land-cover classification. Photogramm. Eng. Rem. Sens. 63(5):535-554.
41. PEDDLE, D.R., G.M. FOODY, A. ZHANG, S.E. FRANKLIN, AND E.F. LEDREW. 1994. Multisource image classification II: An empirical comparison of evidential reasoning, linear discriminant analysis, and maximum likelihood algorithms for alpine land cover classification. Can. J. Rem. Sens. 20:397-408.
42. PENG, C., AND X. WEN. 1999. Recent applications of artificial neural networks in forest resource management: an overview. P. 15-22 in Environmental Decision Support Systems and Artificial Intelligence, Corte, U., and M. Sanchez-Marre (eds.). Tech. Rep. WS-99-07, AAAI Press, Menlo Park, California.
43. POWELL, D.S., AND D.R. DICKSON. 1984. Forest statistics for Maine 1971 and 1982. USDA For. Serv. Northeast. Res. Stat. Resour. Bull. NE-81.
44. RITTER, N.D. AND G.F. HEPNER. 1990. Application of an artificial neural network to land-cover classification of thematic mapper imagery. Comput. Geosci. 16(6):873-880.
45. ROSENFIELD, G.H., AND K. FITZPATRICK-LINS. 1986. A coefficient of agreement as a measure of thematic classification accuracy. Photogramm. Eng. Rem. Sens. 52:223-227.
46. RUMELHART, D.E., G.E. HINTON, AND R.J. WILLIAMS. 1987. Learning internal representations by error propagation. P. 318-362 in Parallel Distributed Processing-Explorations in the Microstructure of Cognition, Vol.1. Rumelhart, D.E., J.L. McClelland, and PDP Research Group (eds.). The MIT Press: Massachusetts.
47. SARLE, W.S. 1994. Neural networks and statistical methods. In Proc. of the Nineteenth Annu. SAS Users Group Internat. Conf. SAS Institute., Cary, NC.
48. SCHWAIGER, R., R. HUBER, AND H.A. MAYER. 1995. Land cover classification of Landsat images using problem-adapted artificial neural networks. P. 33-41 in Proc. of Soft Comput. in Rem. Sens. Data Anal. Binaghi, E., et al. (eds.). World Scientific, Milano, Italy.
49. SHIN, M., AND A.L. GOEL. 2000. Empirical data modeling in software engineering using radial basis functions. IEEE Transact. on Software Eng. 26:1-10.
50. SKIDMORE, A.K., AND B.J. TURNER. 1988. Forest mapping accuracies are improved using a supervised non-parametric classifier. Photogramm. Eng. Rem. Sens. 54:1415-1421.
51. SOLOMON, D.S., D.A. HERMAN, AND W.B. LEAK. 1995. FIBER 3.0: An ecological growth model for northeastern forest types. USDA For. Serv. Gen. Tech. Rep. NE-204.
52. st century. Hansen, M., and T. Burk (eds.). USDA For. Serv. Gen. Tech. Rep. NC-212.
53. SUI, D.Z. 1994. Recent application of neural networks for spatial data handling. Can. J. Rem Sens. 20:368-380.
54. WANG, F. 1990. Fuzzy supervised classification of remote sensing images. IEEE Transact. on Geosci. and Rem.Sens. 28:194-201.
55. WARNER, B., AND M. MISRA. 1996. Understanding neural networks as statistical tools. Am. Stat. 50(4):284-293.
56. ZHANG, J., AND G.M. FOODY. 2001. Fully-fuzzy supervised classification of suburban land cover from remotely sensed imagery: statistical and artificial neural network approaches. Internat. J. Rem. Sens. 22(4):615-628.