MEASURING CLASSIFICATION ACCURACIES USING IMPROVED THERMAL INFRARED DATA
DOI:
https://doi.org/10.47839/ijc.2.3.244Keywords:
Artificial Neural Networks, Classification Accuracy, Maximum Likelihood Approach, Thermal Infrared DataAbstract
Image classification is one of the major aspects in digital image analysis of remotely sensed data. In this paper, we present the effects on classification accuracy if improved thermal data are used instead of raw thermal data. We use two methods, Artificial Neural Networks (ANN) and Maximum Likelihood Approach (MLH) to demonstrate our purpose. Using each method different combinations of raw and improved data are tested to classify in order to compare the accuracies. As a final note, the findings are discussed.References
Stephen M. Leak And Venugopal, G. ‘Thematic Mapper Thermal Infrared Data in Discriminating Selected Urban Features’. International Journal of Remote Sensing, Vol-11, No.5, pp. 841-857, 1990.
Sabins F. Floyd ‘Remote Sensing Principles and Interpretation’, Third Edition. W. H. Freeman and Company, New York, 1997.
Price C. John ‘The Contribution of Thermal Data in Landsat Multi Spectral Classification’. Photogrammetric Engineering and Remote Sensing, Vol-47, No-2, pp. 229-236, 1981.
Ormsbay P. James, ‘The Use of Landsat-3 Thermal Data to Help Differentiate Land Covers’. Remote sensing of environment, vol-12, pp. 92-105, 1982.
Heermann, P. D., and Khazenie, K., ‘Classification of Multispectral Remote Sensing Data using a Back Propagation Neural Network’, IEEE Transactions on Geoscience and Remote Sensing, Vol. 30, No.1, pp.81-88, 1992.
Bischof, H., Schneider, W., and Pinz, A.J., ‘Multispectral Classification of Landsat-Images Using NEURAL Networks’, IEEE Transactions on Geoscience and Remote Sensing, Vol. 30, No.3, pp.483-490, 1992.
Paola, J.D.,and Robert, A.S., ‘A detailed comparison of back-propagation neural network and maximum likelihood classifiers for urban land use classification’, IEEE Transactions on Geoscience and remote sensing, vol.33, no.4, pp 981-996, 1995.
Arora, M.K., and Foody, G.M., ‘Log linear modeling for the evaluation of the variables affecting the accuracy of probabilistic, fuzzy and neural network classifications’, International Journal of Remote sensing, Vol.18, N0.4, pp.785-798, 1997.
Foody, G.M., and Arora, M.K., ‘An Evaluation of some factors affecting the accuracy of classification by an Artificial Neural Network’, International Journal of Remote sensing, Vol.18, N0.4, pp.799-810, 1997
Benediktsson, J.A., and Sveinson, J.R., ‘Feature extraction for multisource data classification with Artificial Neural networks’, International Journal of Remote sensing, Vol.18, N0.4, pp.727-740, 1997.
Chavez S Pat, Jr., Stuart C. Sides, and Labrey A. Anderson ‘Comparison of Three Different Methods to Merge Multi Resolution and Multi Spectral Data: Landsat TM Data and SPOT Panchromatic. Photogrammetric Engineering and Remote Sensing, Vol-57, No-3, pp. 295-303, 1991.
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