23.5 Conclusions
Neural networks are powerful general purpose computing tools. They have become popular in the analysis of remotely sensed data, particularly for classification and regression-type problems in which they have often been demonstrated to extract information more accurately than conventional methods. Although not free from problems, it seems likely that neural networks will be used increasingly in ecological research using remote sensing. Moreover, as some of the problems encountered in use of neural networks arise from a tendency to focus upon the MLP only it is likely that there will be a greater use of other network types. In addition, it is expected that the range of applications of neural networks in remote sensing will broaden. Applications in which neural networks have already been used and increased usage may be expected include: image preprocessing (e.g. geometric, atmospheric and radiometric correction), stereo-matching imagery, image compression, feature extraction, map generalisation, multi-source data analysis, data fusion and image sharpening (e.g. Day, 1997; Foody, 1999a). Thus while neural networks have rapidly become established in remote sensing it is likely that they will be used increasingly and in a broader range of activities that will help exploit more fully the potential of remote sensing as a useful tool in ecological research.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Aleksander I, Morton H (1990) An Introduction to Neural Computing, Chapman and Hall, London.
Atkinson PM, Tatnall ARL (1997) Neural networks in remote sensing, International Journal of Remote Sensing, 18, 711–725.
Baret F (1995) Use of spectral reflectance variation to retrieve canopy biophysical characteristics, In: Danson FM, Plummer SP (eds) Advances in Environmental Remote Sensing, Wiley, Chichester, pp. 33–51.
Benediktsson JA, Swain PH, Ersoy OK (1990) Neural network approaches versus statistical methods in classification of multisource remote sensing data, IEEE Transactions on Geoscience and Remote Sensing, 28, 540–551.
Bishop CM (1995) Neural Networks for Pattern Recognition Oxford University Press, Oxford.
Boyd DS, Foody GM, Ripple WJ (2002) Evaluation of approaches for forest cover estimation in the Pacific Nortwest, USA using remote sensing, Applied Geography, 22, 375–392.
Bruzzone L, Prieto DF (1999) A technique for the selection of kernel-function parameters in RBF neural networks for classification of remote sensing images, IEEE Transactions on Geoscience and Remote Sensing, 37: 1179–1184.
Campbell JB (2002) Introduction to Remote Sensing, third edition, Taylor and Francis, London.
Carpenter GA, Gopal S, Macomber S, Martens S, Woodcock CE (1999a) A neural network method for mixture estimation for vegetation mapping, Remote Sensing of Environment, 70, 138–152.
Carpenter GA, Gopal S, Macomber S, Martens S, Woodcock CE, Franklin J (1999b) A neural network method for efficient vegetation mapping, Remote Sensing of Environment, 70, 326–338.
Corne SA, Carver SJ, Kunin WE, Lennon JJ, van Hees WWS (2004) Predicting forest attributes in southeast Alaska using artificial neural networks, Forest Science, 50, 259–276.
Cote S, Tatnall ARL (1997) The Hopfield neural network as a tool for feature tracking and recognition from satellite sensor images, International Journal of Remote Sensing, 18, 871–885.
Curran PJ, Dungan JL, Peterson DL (2001) Estimating the foliar biochemical concentration of leaves with reflectance spectrometry. Testing the Kokaly and Clark methodologies, Remote Sensing of Environment, 76, 349–359.
Davalo E, Naim P (1991) Neural Networks, Macmillan, Basingstoke.
Day C (1997) Remote sensing applications which may be addressed by neural networks using parallel processing technology, In Kanellopoulos, I, Wilkinson, G. G., Roli, F., Austin, J. (eds) Neuro-computation in Remote Sensing Data Analysis, Springer, Berlin, pp. 262–279.
Estes J Belward A, Loveland T, Scepan J, Strahler A, Townshend J, Justice C (1999), The way forward, Photogrammetric Engineering and Remote Sensing, 65, 1089–1093.
Fardanesh MT, Ersoy OK (1998) Classification accuracy improvement of neural network classifiers by using unlabeled data, IEEE Transactions on Geoscience and Remote Sensing, 36, 1020–1025.
Fischer MM (1998) Computational neural networks a new paradigm for spatial analysis, Environment and Planning A, 30, 1873–1891.
Fischer MM, Gopal S, Staufer P, Steinnocher K (1997) Evaluation of neural pattern classifiers for a remote sensing application, Geographical Systems, 4, 195–225.
Foody GM (1996) Approaches for the production and evaluation of fuzzy land cover classifications from remotely-sensed data, International Journal of Remote Sensing, 17, 1317–1340.
Foody GM (1999a) Image classification with a neural network: from completely-crisp to fully-fuzzy situations, In Atkinson, P. M. and Tate, N. J. (eds) Advances in Remote Sensing and GIS, Wiley, Chichester, pp. 17–37.
Foody GM (1999b) The significance of border training patterns in classification by a feedforward neural network using backpropagation learning, International Journal of Remote Sensing, 20, 3549–3562.
Foody GM (1999c) The continuum of classification fuzziness in thematic mapping, Photogrammetric Engineering and Remote Sensing, 65, 443–451.
Foody GM (2001) Thematic mapping from remotely sensed data with neural networks: MLP, RBF and PNN based approaches, Journal of Geographical Systems, 3, 217–232.
Foody GM (2004a) Supervised classification by MLP and RBF neural networks with and without an exhaustively defined set of classes, International Journal of Remote Sensing, 25, 3091–3104.
Foody GM (2004b) Thematic map comparison: evaluating the statistical significance of differences in classification accuracy, Photogrammetric Engineering and Remote Sensing, 70, 627–633.
Foody GM, Arora MK (1997) An evaluation of some factors affecting the accuracy of classification by an artificial neural network, International Journal of Remote Sensing, 18, 799–810.
Foody GM, McCulloch MB, Yates WB (1995) Classification of remotely sensed data by an artificial neural network: issues related to training data characteristics. Photogrammetric Engineering and Remote Sensing, 61, 391–401.
Foody GM, Lucas RM, Curran PJ, Honzak M (1997) Non-linear mixture modelling without end-members using an artificial neural network, International Journal of Remote Sensing, 18, 937–953.
Foody GM, Cutler ME, McMorrow J, Pelz D, Tangki H, Boyd DS, Douglas I (2001) Mapping the biomass of Bornean tropical rain forest from remotely sensed data, Global Ecology and Biogeography, 10, 379–387.
Friedl MA, Woodcock C, Gopal S, Muchoney D, Strahler AH, Barker-Schaaf C (2000) A note on procedures used for accuracy assessment in land cover maps derived from AVHRR data, International Journal of Remote Sensing, 21, 1073–1077.
Gamba P, Houshmand B (2001) An efficient neural classification chain of SAR and optical urban images, International Journal of Remote Sensing, 22, 1535–1553.
Gopal S, Woodcock CE, Strahler AH (1999) Fuzzy neural network classification of global land cover from a 1° AVHRR data set, Remote Sensing of Environment, 67, 230–243.
Hall RJ (2000) Applications of remote sensing to forestry — current and future, Forestry Chronicle, 76, 855–857.
Ito Y, Omatu S. (1997) Category classification method using a self-organising neural network, International Journal of Remote Sensing, 18, 829–845.
Ji CY (2000) Land-use classification of remotely sensed data using Kohonen self-organising feature map neural networks, Photogrammetric Engineering and Remote Sensing, 66, 1451–1460.
Jin YQ, Liu C (1997) Biomass retrieval from high-dimensional active/passive remote sensing data by using artificial neural networks, International Journal of Remote Sensing, 18, 971–979.
Kanellopoulos I, Wilkinson GG (1997) Strategies and best practice for neural network image classification, International Journal of Remote Sensing, 18, 711–725.
Kasischke ES, Melack JM. Dobson MC (1997) The use of imaging radars for ecological applications —a review, Remote Sensing of Environment, 59, 141–156.
Kavzoglu T, Mather PM (2003) The use of backpropagating artificial neural networks in land cover classification, International Journal of Remote Sensing, 24, 4907–4938.
Kimes DS, Nelson RF, Fifer ST (2000) Predicting ecologically important vegetation variables from remotely sensed optical/radar data using neuronal networks, In Lek, S. and Guegan, J-F. (eds) Artificial Neuronal Networks: Applications to Ecology and Evolution, Springer, Berlin, pp. 31–44.
Lawrence RL, Ripple WJ (1998) Comparisons among vegetation indices and bandwise regression in a highly disturbed heterogeneous landscape: Mount St. Helens, Washington, Remote Sensing of Environment, 64, 91–102.
Lek S, Giraudel JL, Guegan JF (2000) Neuronal networks: algorithms and architectures for ecologists and evolutionary ecologists, In Lek, S. and Guegan, J-F. (eds) Artificial Neuronal Networks: Applications to Ecology and Evolution, Springer, Berlin, pp. 3–27.
Lillesand TM, Kiefer RW, Chipman JW (2000) Remote Seining and Image Interpretation, fifth edition, Wiley, New York.
Liu WG, Seto KC, Wu EY, Gopal S, Woodcock CE (2004) ART-MMAP: A neural network approach to subpixel classification, IEEE Transactions on Geoscience and Remote Sensing, 42, 1976–1983.
Loveland TR, Zhu Z, Ohlen DO, Brown JF, Reed BC, Yang L (1999) An analysis of the IGBP global land-cover characterisation process, Photogrammetric Engineering and Remote Sensing, 65, 1021–1032.
Lucas NS, Curran PJ (1999) Forest ecosystem simulation modelling: the role of remote sensing, Progress in Physical Geography, 23, 391–423.
Mather PM (2004) Computer Processing of Remotely-Sensed Images, third edition, Wiley, Chichester.
Pal M, Mather (2004) Assessment of the effectiveness of support vector machines for hyperspectral data, Future Generation Computer Systems, 20, 1215–1225.
Paola JD, Schowengerdt RA (1995) A detailed comparison of backpropagation neural network and maximum likelihood classification for urban land use classification, IEEE Transactions on Geoscience and Remote Sensing, 33, 981–996.
Peddle DR, Foody GM, Zhang A, Franklin SE, LeDrew EF (1994) Multisource image classification II: an empirical comparison of evidential reasoning and neural network approaches, Canadian Journal of Remote Sensing, 20, 396–407.
Poth A, Klaus MV, Stein G (2001) Optimisation at multi-spectral land cover classification with fuzzy clustering and the Kohonen feature map, International Journal of Remote Sensing, 22, 1423–1439.
Rollet R, Benie GB, Li W, Wang S, Boucher JM (1998) Image classification algorithm based on the RBF neural network and K-means, International Journal of Remote Sensing, 19, 3003–3009.
Roughgarden J, Running SW, Matson PA (1991) What does remote sensing for ecology? Ecology, 72, 1918–1922.
Scepan J (1999) Thematic validation of high-resolution global land-cover data sets, Photogrammetric Engineering and Remote Sensing, 65, 1051–1060.
Schalkoff R (1992) Pattern Recognition: Statistical, Structural and Neural Approaches Wiley, New York.
Schowengerdt RA (1997) Remote Sensing: Models and Methods for Image Processing, second edition, Academic Press, San Diego.
Shahshahani BM, Landgrebe DA (1994) The effect of unlabeled samples in reducing the small sample-size problem and mitigating the Hughes phenomenon, IEEE Transactions on Geoscience and Remote Sensing, 32, 1087–1095.
Smith JA (1993) LAI inversion using a back-propagating neural network trained with a multiple scattering model, IEEE Transactions on Geoscience and Remote Sensing, 31, 1102–1106.
Staufer P, Fischer MM (1997) Spectral pattern recognition by a two-layer perceptron: effects of training set size, In: Kanellopoulos, I., Wilkinson GG, Roli F, Austin J (eds) Neuro-computation in Remote Sensing Data Analysis, Springer, Berlin, pp. 105–116.
Strahler AH (1980) The use of prior probabilities in maximum likelihood classification of remotely sensed data, Remote Sensing of Environment, 10, 135–163.
Specht DF (1990) Probabilistic neural networks, Neural Networks, 3, 109–118.
Tatem AJ, Lewis HG., Atkinson PM, Nixon MS (2001) Super-resolution target identification from remotely sensed images using a Hopfield neural network, IEEE Transactions on Geoscience and Remote Sensing, 39, 781–796.
Tatem AJ, Lewis HG, Atkinson PM, Nixon MS (2003) Increasing the spatial resolution of agricultural land cover maps using a Hopfield neural network, International Journal of Geographical Information Science, 17, 647–672.
Townshend JRG (1992) Land cover, International Journal of Remote Sensing, 13, 1319–1328.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Foody, G.M. (2006). Pattern Recognition and Classification of Remotely Sensed Images by Artificial Neural Networks. In: Recknagel, F. (eds) Ecological Informatics. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-28426-5_23
Download citation
DOI: https://doi.org/10.1007/3-540-28426-5_23
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28383-6
Online ISBN: 978-3-540-28426-0
eBook Packages: Earth and Environmental ScienceEarth and Environmental Science (R0)