Comparison of maximum likelihood classification method with supervised artificial neural network algorithms for land use activities

References

• Benedictsson , 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 and Leonardis , A . 1998 . Finding optimal neural networks for land use classification . IEEE Transactions on Geoscience and Remote Sensing , 36 : 337 – 341 .
   Web of Science ®Google Scholar
• Bridle J. S 1990 Probabilistic interpretation of feedforward classification network outputs, with relations to statistical pattern recognition In Neurocomputing: Algorithms, Architectures and Applications (Berlin: Springer-Verlag)
   Google Scholar
• Bruzzone , L and Serpico , S. B . 1997 . An interactive technique for the detection of land cover transitions in multitemporal remote sensing images . IEEE Transactions on Geoscience and Remote Sensing , 35 : 858 – 867 .
   Web of Science ®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 & Remote Sensing , 63 : 523 – 533 .
   Web of Science ®Google Scholar
• Civco D. L Wang Y 1994 Classification of multispectral, multitemporal, multisource spatial data using artificial neural networks ASPRS/ACSM, http://wwwsgi.ursus.maine.edu/gisweb/spatdb/acsm/ac94014.html
   Google Scholar
• Coppin , B. R and Bauer , M. E . 1996 . Digital change detection in forest ecosystems with remote sensing imagery . Remote Sensing Reviews , 13 : 207 – 234 .
   Google Scholar
• Dai , X. L and Khorram , S . 1998 . The effects of image misregistration on the accuracy of remotely sensed change detection . IEEE Transactions on Geoscience and Remote Sensing , 36 : 1566 – 1577 .
   Web of Science ®Google Scholar
• Dai , X. L and Khorram , S . 1999 . Remotely sensed change detection based on artificial neural networks . Photogrammetric Engineering & Remote Sensing , 65 : 1187 – 1194 .
   Web of Science ®Google Scholar
• DeSieno D 1988 Adding a conscience to competitive learning International Joint Conference on Neural Networks (San Diego, CA: IEEE) pp. 117–124
   Google Scholar
• Foody , G. M , Campbell , N. A , Trood , N. M and Wood , T. F . 1992 . Derivation and application of probabilistic measures of class membership from the maximum likelihood classification . Photogrammetric Engineering & Remote Sensing , 59 : 1335 – 1341 .
   Google Scholar
• Gersho , A . 1982 . On the structure of vector quantizers . IEEE Transactions on Information Theory, 17–28 , 2 : 157 – 166 .
   Web of Science ®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 & 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 & Remote Sensing , 62 : 1249 – 1260 .
   Web of Science ®Google Scholar
• Gonzales R. C Woods R. E 1992 Digital Image Processing (Addison-Wesley)
   Google Scholar
• Hagan M. T Demuth H. B Beale M 1996 Neural Network Design (Boston PWS Publishing Company)
   Google Scholar
• Hayes , D. J and Sader , S. A . 2001 . Comparison Of change-detection techniques for monitoring tropical forest clearing and vegetation regrowth in a time series . Photogrammetric Engineering & Remote Sensing , 67 : 1067 – 1075 .
   Web of Science ®Google Scholar
• Haykin S 1994 Neural Networks: A Comprehensive Foundation (Macmillan College Publishing Company)
   Google Scholar
• Hecht-Nielsen R 1990 Neurocomputing (Reading, MA: Addison-Wesley)
   Google Scholar
• Janssen , L. L. F and Vanderwel , J. M . 1994 . Accuracy assessment of satellite derived land-cover data: a review . Photogrammetric Engineering & Remote Sensing , 60 : 419 – 426 .
   Web of Science ®Google Scholar
• Jensen J. R 1996 Introductory Digital Image Processing: A Remote Sensing Perspective (NJ: Prentice Hall)
   Google Scholar
• Kaminsky , E. J , Barad , H and Brown , W . 1997 . Textural neural network and version space classifiers for remote sensing . International Journal of Remote Sensing , 18 : 741 – 762 .
   Web of Science ®Google Scholar
• Kavzog˘lu T 2001 An investigation of the design and use of feed-forward artificial neural networks in the classification of remotely sensed images, Ph.D. Thesis, University of Nottingham
   Google Scholar
• Kohonen T 1984 Self Organization and Associative Memories (New York, NY: Springer Verlag)
   Google Scholar
• Kohonen T Barna G Chrisley R 1988 Statistical pattern recognition with neural networks: benchmarking studies International Joint Conference on Neural Networks, San Diego, CA, pp. 61–68
   Google Scholar
• Martin , L. R. G and Howarth , P. J . 1989 . Change-detection accuracy assessment using SPOT multispectral imagery of the rural-urban fringe . Remote Sensing of Environment , 30 : 55 – 66 .
   Web of Science ®Google Scholar
• Özkan C 2001 The classification of satellite imagery data with artificial neural networks, Ph.D. Thesis, Istanbul Technical University
   Google Scholar
• Özkan C Sunar F 1999 The use and effectiveness of artificial neural networks in forest fire classification RSS'99 Symposium on Earth Observation: From Data to Information, 8–10 September, Cardiff, pp. 767–772
   Google Scholar
• Paola J. D 1994 Neural Network Classification of Multispectral Imagery, M.Sc. Thesis, University of Arizona
   Google Scholar
• Paola , J. D and Schowengerdt , R. A . 1995 . A detailed comparison of backpropagation neural network and maximum-likelihood classifiers for urban land use classification . IEEE Transactions on the Geosciences and Remote Sensing , 33 : 981 – 996 .
   Web of Science ®Google Scholar
• Schowengerdt R. A 1997 Remote Sensing: Models and Methods for Image Processing (San Diego, CA: Academic Press)
   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 & Remote Sensing , 63 : 501 – 514 .
   Web of Science ®Google Scholar
• Simpson P. K 1990 Artificial Neural Systems: Foundations, Paradigms, Applications, and Implementations (New York, NY: Pergamon Press) pp. 3–14
   Google Scholar
• Sunar , F . 1998 . Analysis of changes in multidate dataset: a case study in l˙kitelli area, Istanbul, Turkey . International Journal of Remote Sensing , 19 : 225 – 235 .
   Web of Science ®Google Scholar
• Taberner M Sunar F 1998 An implementation of the change vector analysis technique to assess the changes in land cover with multidate remotely sensed images, a case study: Istanbul, Turkey 27th International Symposium on Remote Sensing of Environment – Information for Sustainability, 8–12 June, Tromso, Norway, pp. 454–457
   Google Scholar