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Dissertation II: Geo-Techniques

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Research Design and Proposal Writing in Spatial Science

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Abstract

This proposal is an example of a technique driven proposal. The proposal is a fine example of the use of graphics to simplify complex methods of analysis and the effectiveness of graphics to convey information. Additionally, the literature review clearly defines the contribution of the research and is presented in a straightforward fashion using a table structure.

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References

  • Anderson, Dave and McNeil, George, 1992. Artificial Neural Network Technology. Rome, NY: DACS. http://www.dacs.dtic.mil/techs/neural/neural.title.html.

    Google Scholar 

  • Ardo, J., Pilesjo, P., and Skidmore, A. (1997), Neural networks, multitemporal Landsat Thematic Mapper data and topographic data to classify forest damage in the Czech Republic. Canadian Journal of Remote Sensing, 31: 217–219.

    Google Scholar 

  • Bandibas, Joel C., (1998). Combining The Spectral and Spatial Signature of Information Classes using Artificial Neural Network Based Classifier for Remote Sensing of Spatially Heterogeneous Land-Use/Land Cover System in the Tropics. Proceedings of the 19th Asian Conference on Remote Sensing, Manila, Philippine, November 16–20, 1998. http://www.gisdevelopment.net/aars/acrs/1998/ts9/ts9002.shtml.

    Google Scholar 

  • Benediktsson, J. A. and Sveinsson, J. R. (1997), Multisource data classification and feature extraction with neural networks. International Journal of Remote Sensing, 18: 727–740.

    Google Scholar 

  • Benediktsson, J. A., Sveinsson, J. R., and Ersoy, O. K. (1997), Parallel consensual neural networks. IEEE Transactions on Neural Networks, 8(1): 54–64.

    Article  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.

    Article  Google Scholar 

  • Benediktsson, J. A., Swain, P. H., and Ersoy, O. K. 1993, Conjugate-gradient neural networks in classification of multisource and very-high-dimensional remote sensing data. International Journal of Remote Sensing, 14: 2883–2903.

    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, 30:482–490.

    Article  Google Scholar 

  • Brisco, B. and Brown, R. J., (1995), Multidate SAR/TM synergism for crop classification in Western Canada. Photogrammetric Engineering & Remote Sensing, 61(8): 1009–14.

    Google Scholar 

  • Bruzzone, L., Conese, C., and Maselli, F. (1997), Multisource classification of complex rural areas by statistical and neural-network approaches. Photogrammetric Engineering & Remote Sensing, 63:523–33.

    Google Scholar 

  • Canny, J., 1986. A computational approach to edge detection. IEEE Trans. Pattern Analysis and Machine Intelligence, 8(6): 679–698, 1986

    Google Scholar 

  • Carpenter, G. A. and Grossberg, S., (1987), A massively parallel architecture for a self-organizing neural pattern recognition machine. Computer Vision, Graphics, and Image Processing, 37:54–115.

    Article  Google Scholar 

  • Carpenter, G. A. and Grossberg, S., (1992), A self-organizing neural network for supervised learning, recognition and prediction. IEEE Communication Magazine, 38–49.

    Google Scholar 

  • Chen, K. S., Tzeng, Y. C., and Chen, C. F. (1995), Land-cover classification of multi-spectral imagery using a dynamic learning neural network. Photogrammetric Engineering and Remote Sensing, 61(4):403–8.

    Google Scholar 

  • Chen, S., Chen, C., and Chen, M. (1998), Neural-fuzzy classification for segmentation of remotely sensed images. IEEE Transactions on Signal Processing, 45:2639–54.

    Google Scholar 

  • Cromp, R. F. (1991), Automated extraction of metadata from remotely sensed satellite imagery. In Proceedings, ASPIR-ACSM Annual Meeting, Baltimore, MD, 3:67–77.

    Google Scholar 

  • Dai, X. L., and Khorran, S. (1999), Remotely sensed change detection based on artificial neural networks. Photogrammetric Engineering & Remote Sensing, 65(10):1187–94.

    Google Scholar 

  • Dowla, F. U., and Rogers, L. L. (1995) Solving problems in environmental engineering and geosciences with artificial neural networks. Mssachusetts Institute of Technology, Boston. 239p.

    Google Scholar 

  • Fauzi, Anas, Hussin, Yousif Ali, and Weir, Michael, (2001), A comparison between neural networks and maximum likelihood remote sensed data classifiers to detect tropical rain logged-over forest in Indonesia. The 22nd Asian Conference on Remote Sensing, 5–9 November 2001, Singapore. http://www.crisp.nus.edu.sg/~acrs2001/pdf/016fauzi.pdf

    Google Scholar 

  • Fitzgerald, R. W., and Lees, B. G. (1996), Temporal context in floristic classification. Computers and Geosciences, 22(11):981–94.

    Google Scholar 

  • Foody, G. M. (1995a), Using prior knowledge in artificial neural network classification with a minimal training set. International Journal of Remote Sensing, 16:301–312.

    Google Scholar 

  • Foody, G. M. (1995b), Land cover classification using an artificial neural network with ancillary information. International Journal of Geographical Information Systems, 9:527–542.

    Google Scholar 

  • Foody, G. M., and Arora, M. K. (1997), An evaluation of some factors affecting the accuracy of classification by an artificial neural network. International Journal of Remote Sensing, 18(4):799–810.

    Google Scholar 

  • Foody, G. M., and Boyd, D. S., 1996. Fuzzy Mapping of Tropical Land Cover from Remotely Sensed Data: the Use of an Artificial Neural Network for Sub-Pixel Land Cover Estimation. Extended Abstracts from the 1st International Conference on Geo-Computation, University of Leeds, United Kingdom, 17–19 September 1996. http://www.ashville.demon.co.uk/gc1996/abs034.htm.

    Google Scholar 

  • Foody, G. M., and Boyd, D. S. (1999), Detection of partial land cover change associated with the migration of inter-class transitional zones. International Journal of Remote Sensing, 20(14):2723–40.

    Google Scholar 

  • Foschi, P. G., and Smith, D. K. (1997), Detecting subpixel woody vegetation in digital imagery using two artificial intelligence approaches. Photogrammetric Engineering and Remote Sensing, 63(5):493–500.

    Google Scholar 

  • Frizzelle, Brian G. and Moody, Aaron, (2001), Mapping continuous distributions of land cover: a comparison of maximum-likelihood estimation and artificial neural networks. Photogrammetric Engineering & Remote Sensing, 67(6):693–705.

    Google Scholar 

  • German, G. W. H., and Gahegan, M. N. (1996), Neural network architectures fro the classification of temporal image sequences. Computers and Geosciences, 22:969–979.

    Article  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(5): 513–23.

    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: 1249–60.

    Google Scholar 

  • Gurney, Kevin (1997), An Introduction to Neural Networks. London: UCL Press Limited. 234p.

    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.

    Article  Google Scholar 

  • Hornik, R., and Sejnowski, T. (1988), Multilayer feedforward networks are universal approximators. Neural Networks, 2:359–366.

    Google Scholar 

  • Huang, K. and Mausel, P. M., (1994), Comparing a piecewise linear classifier with Gaussian maximum-likelihood and parallelepiped classifiers in terms of accuracy and speed. Photogrammetric Engineering & Remote Sensing, 60(11): 1333–8.

    Google Scholar 

  • Irons, J.R., B.L. Markham, R.F. Nelson, D.L. Toll, D.L. Williams, R.S. Latty, and M.L. Stauffer, 1985. The effects of spatial resolution on the classification of Thematic Mapper data. International Journal of Remote Sensing, 6(8): 1385–1403.

    Google Scholar 

  • Jensen, J. R. (1986), Introductory digital image processing: A remote sensing perspective. Prentice-Hall, New Jersey, USA. P379.

    Google Scholar 

  • Kanellopoulos, I., and Wilkinson, G. G. (1997), Strategies and best practice for neural network image classification. International Journal of Remote Sensing, 18(4):711–25.

    Google Scholar 

  • Kanellopoulos, I., Varas, 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.

    Google Scholar 

  • Key, J., Maslanic, A., and Schweiger, A. J. (1989), Classification of merged AVHRR and SMMR arctic data with neural networks. Photogrammetric Engineering and Remote Sensing, 55:1331–8.

    Google Scholar 

  • Kimes, D. S., Holben, B. N., Nickeson, J. E., and Mckee, A. (1996), Extracting forest age in a Pacific Northwest forest from thematic mapper and topographic data. Remote Sensing of Environment, 56:133–140.

    Article  Google Scholar 

  • Kimes, D. S., Nelson, R. F., Manry, M. T., and Fung, A. K. (1998), Attributes of neural networks for extracting continuous vegetation variables from optical and radar measurements. International Journal of Remote Sensing, 19(14):2639–63.

    Google Scholar 

  • Kimes, D. S., Nelson, R. F., Salas, W. A., and Skoles, D. L. (1999), Mapping secondary tropical forest and forest age from SPOT HRV data. International Journal of Remote Sensing, 20(18):3625–40.

    Google Scholar 

  • Kung, S. Y., and Tuar, J. S., 1996, Decision-based neural networks with signal/image classification applications. IEEE Transactions on Neural Networks, 6:170–81.

    Google Scholar 

  • Latty, R.S., and R.M. Hoffer, 1981. Computer-based classification accuracy due to the spatial resolution using per-point versus per-field classification techniques. Symposium of Machine Processing of Remotely Sensed Data, pp. 384–392.

    Google Scholar 

  • Lee, J., Weger, R. C., Sengupta, S. K., and Welsch, R. M. (1990), A neural network approach to cloud classification. IEEE Transactions on Geoscience and Remote Sensing, 28:846–855.

    Google Scholar 

  • Lippmann, R. P. (1987), An introduction to computing with neural networks. IEEE ASSP Magazine, pp. 713–715.

    Google Scholar 

  • Liu, Z., and Xiao, J., (1991), Classification of remotely sensed image data using artificial neural networks. International Journal of Remote Sensing, 12(11):2433–8.

    Google Scholar 

  • Markham, B.L., and J.R.G. Townshend, 1981. Land cover classification accuracy as a function of sensor spatial resolution. Proceedings of the Fifteenth International Symposium on Remote Sensing of Environment, Ann Arbor, Michigan, pp.1075–1090.

    Google Scholar 

  • Marceau, Daniel J. and Hay, Geoffrey J., 1999. Remote sensing contributions to the scale issue. Canadian Journal of Remote Sensing, 25(4): 357–366.

    Google Scholar 

  • Maselli, F., Conese, C., de Filippis, T. and Romani, M., (1995). Integration of ancillary data into a maximum likelihood classification with nonparametric priors. ISPRS Journal of Photogrammetry and Remote Sensing, 50: 2–11.

    Article  Google Scholar 

  • Mather, P. (1999), Computer processing of remotely sensed images: an introduction (2nd ed.). John Wiley & Sons Ltd, West Sussex, England. 292p.

    Google Scholar 

  • Mather, P. M., (1999), Computer Processing of Remotely Sensed Images: An Introduction (2nd ed.). ISBN 0-471-98550-3. Chicester: John Wiley & Sons Ltd. 292p.

    Google Scholar 

  • Mausel, P., Wu, Y., Li, Y., Moran, E. F., and Brondizio, E. S. (1993), Spectral Identification of successional stages following deforestation in the Amazon. Geocarto International, 8(4):61–71.

    Google Scholar 

  • Meyer, P., Staenz, K., and Itten, K. I., (1996), Semi-automated procedures for tree species identification in high spatial resolution data from digitized colour infrared-aerial photography. ISPRS Journal of Photogrammetry and Remote Sensing, 51(1): 5–16.

    Google Scholar 

  • Miller, D. M., Kaminsky, E. J. and Rana, S. (1995), Neural network classification of remote-sensing data. Computers and Geosciences, 21(4):377–86.

    Google Scholar 

  • Moran, E. F., Brondizio, E., and Mausel, P. (1994), Secondary succession: Monitoring secondary succession and land-use change in Amazonia. National Geographic Research & Exploration, 10(4):458–476.

    Google Scholar 

  • Muchoney, Doug and Williamson, James (2001), A Gaussian adaptive resonance theory neural net work classification algorithms applied to supervised land cover mapping using multitemporal vegetation index data. IEEE Transactions on Geoscience and Remote Sensing, 39(9): 1969–77.

    Article  Google Scholar 

  • Olmsted, D. D. (1999a), History and principles of neural networks to 1960. http://www.neurocomputing.org/

    Google Scholar 

  • Olmsted, D. D. (1999b), History and principles of neural networks from 1960 to present. http://www.neurocomputing.org/

    Google Scholar 

  • Paola, J. and Schowengerdt, R. A., (1995a), A detailed comparison of back-propagation neural networks and maximum-likelihood classifiers for urban land use classification. IEEE Transactions on Geoscience and Remote Sensing, 33: 981–996.

    Article  Google Scholar 

  • Paola, J. and Schowengerdt, R. A., (1995b). A review and analysis of back-propagation neural networks for classification of remotely-sensed multi-spectral imagery. International Journal of Remote Sensing, 16: 3033–58.

    Google Scholar 

  • Paola, J. D. and Schowengerdt, R. A. (1995), A review and analysis of back-propagation neural networks for classification of remotely-sensed multi-spectral imagery. International Journal of Remote Sensing, 16(16): 3033–58.

    Google Scholar 

  • Price, K. P., Egbert, S. L., Nellis, M. D., Lee, R. and Boyce, R., (1997). Mapping land cover in a high plains agro-ecosystem using a multi-date Landsat Thematic Mapper modeling approach. Transactions of the Kansas Academy of Science, 100(1/2): 21–33.

    Google Scholar 

  • Raghu, P. P., Poongodi, R., and Yegnanarayana, B. (1995), A combined neural network approach for texture classification. Neural Networks, 8(6):975–87.

    Article  Google Scholar 

  • Ranson, K. J. and Sun, G., (1997), An evaluation of AIRSAR and SIR-C/X-SAR data for estimating northern forest attributes. Remote Sensing of Environment, 59: 203–222.

    Article  Google Scholar 

  • Ranson, K. J., Sun, G., Knox, R. G., Levine, E. R., Weishampel, J. F. and Fifer, S. T., (1997). Using coupled models and GIS for understanding northern forest ecosystem dynamics, 4th International Conference on GIS-Beijing’97, Beijing China, 19–22 August.

    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.

    Google Scholar 

  • Skole, D. L., Chomentowski, W. H., Salas, W. A., and Nobre, A. D. (1994), Physical and human dimensions of deforestation in Amazonia. Bioscience, 44(5):314–323.

    Google Scholar 

  • Tag, P. M., Bankert, R. L., and Brody, L. R. (2000), An AVHRR multiple cloud-type classification package. Journal of Applied Meteorology, 39(2):125–134.

    Article  Google Scholar 

  • Tatem, Andrew J., Lewis, Hugh G., and Atkinson, Peter M., (2001), Super-resolution target identification from remotely sensed images using a Hopfield neural network. IEEE Transactions on Geoscience and Remote Sensing, 39(4):781–96.

    Article  Google Scholar 

  • Tian, B., Shaikh, M. A., and Azimi-Sadjadi, M. R. (1999), A study of cloud classification with neural networks using spectral and textural features. IEEE Transactions on Neural Networks, 10(1):138–51.

    Google Scholar 

  • Treitz, P. and Howarth, P., (2000), Integrating spectral, spatial, and terrain variables for forest ecosystem classification. Photogrammetric Engineering and Remote Sensing, 66(3): 305–17.

    Google Scholar 

  • Zurada, J. M. (1992), Introduction to Artificial Neural Systems. PWS Publishing Company, Boston, USA. ISBN 0-534-95460-X. pp. 163–250.

    Google Scholar 

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Authors and Affiliations

  1. Laboratory for Advanced Information Technology & Standards, George Mason University, Lanham, Maryland

    Dr. Genong Yu Ph.D. (Research Assistant)

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  1. Dr. Genong Yu Ph.D.
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Yu, G. (2005). Dissertation II: Geo-Techniques. In: Research Design and Proposal Writing in Spatial Science. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-27953-9_13

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