Abstract
The principal purpose of this Chapter is to present the algorithms used regularly for the supervised classification of single sensor remote sensing image data. These are collected in Part I. When data from a variety of sensors or sources (such as found in the integrated spatial data base of a Geographical Information System) requires analysis, or when the spatial resolution of a sensor is sufficiently high to warrant attention being paid to neighbouring pixels when performing a classification, more sophisticated analysis tools may be required. A range of these is presented in Part II, along with a treatment of the neural network method for image analysis. These techniques are conceptually more difficult than the standard procedures and have been grouped separately for that reason. It is suggested that only Part I be covered on a first reading of the material of this book; Part II can be left safely until the need arises without affecting an understanding of the remaining chapters.
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References for Chapter 8
P. Atkinson, J. L. Cushnie, J. R. Townshend and A. Wilson, 1985: Improving Thematic Mapper Land Cover Classification Using Filtered Data. International Journal of Remote Sensing, 6, 955–961.
J. A. Benediktsson, P.H. Swain and O. K. Esroy, 1990: Neural Network Approaches Versus Statistical Methods in Classification of Multisource Remote Sensing Data. IEEE Trans Geoscience and Remote Sensing, 28, 540–552.
F. Y. Borden, D. N. Applegate, B. J. Turner, B. F. Merembeck, E. G. Crenshaw, H. M. Lachowski and D. N. Thompson, 1977: Satellite and Aircraft Multispectral Scanner Digital Data Users Manual. Technical Report ORSER-SSEL 1–77, Pennsylvania State University.
R. O. Duda and P. E. Hart, 1973: Pattern Classification and Scene Analysis, N. Y., Wiley.
B. C. Forster, 1982: The Derivation of Approximate Equations to Correct for the Landsat MSS Point Spread Function. Proc. Commission 1 (Primary Data Acquisition) Int. Soc. for Photogrammetry and Remote Sensing, Canberra, April, 6–10.
J.E. Freund and R. E. Walpole, 1987: Mathematical Statistics, 4e, New Jersey, Prentice Hall.
T D. Garvey, J. D. Lowrance and M. A. Fisher, 1981: An Inference Technique for Integrating Knowledge from Disparate Sources. Proc. 7th Int. Conf Artifical Intelligence, Vancouver, 319–325.
T.D. Garvey, 1987: Evidential Reasoning for Geographic Evaluation for Helicopter Route Planning. IEEE Trans Geoscience and Remote Sensing, GE-25, 294–304.
P. Gong and P. J. Howarth, 1989: Performance Analyses of Probabilistic Relaxation Methods for Land-Cover Classification. Remote Sensing of Environment, 30, 33–42.
P. Gong and P. J. Howarth, 1990: The Use of Structural Information for Improving Land-Cover Classification Accuracies at the Rural-Urban Fringe. Photogrammetric Engineering and Remote Sensing, 56, 67–73.
R. Harris, 1985: Contextual Classification Post-Processing of Landsat Data Using a Probabilistic Relaxation Model. Int. J. Remote Sensing, 6, 847–866.
G. F. Hepner, 1990: Artificial Neural Network Classification Using a Minimal Training Set: Comparison to Conventional Supervised Classification. Photogrammetric Engineering and Remote Sensing, 56, 469–473.
R. L. Kettig & D. A. Landgrebe, 1976: Classification of Multispectral Image Data by Extraction and Classification of Homogeneous Objects. IEEE Trans. Geoscience Electronics, GE-14, 19–26.
N. Khazenie and M. M. Crawford, 1990: Spatial-Temporal Autocorrelation Model for Contextual Classification. IEEE Trans Geoscience and Remote Sensing, 28, 529–539.
J. Kittler and D. Pairman, 1985: Contextual Pattern Recognition Applied to Cloud Detection and Identification. IEEE Trans Geoscience and Remote Sensing, GE-23, 855–863.
T. Lee, 1984: Multisource Context Classification Methods in Remote Sensing. PhD Thesis, The University of New South Wales, Kensington, Australia.
T. Lee and J. A. Richards, 1985: A low Cost Classifier for Multitemporal Applications. Int. J. Remote Sensing, 6, 1405–1417.
T. Lee, J. A. Richards and P. H. Swain, 1987: Probabilistic and Evidential Approaches for Multi- source Data Analysis. IEEE Trans Geoscience and Remote Sensing, GE-25, 283–293.
T. Lee and J. A. Richards, 1989: Pixel Relaxation Labelling Using a Diminishing Neighbourhood Effect. Proc. IGARSS’89. Vancouver, 634–637.
R. P. Lippmann, 1987: An Introduction to Computing with Neural Nets. IEEE ASSP Magazine, April, 4–22.
Mohn, N., L. Hjort and G. O. Storvik, 1987: A Simulation Study of Some Contextual Classification Methods for Remotely Sensed Data. IEEE Trans Geoscience and Remote Sensing, 25, 796–804.
W. L. Moon, 1990: Integration of Geophysical and Geological Data Using Evidential Belief Function. IEEE Trans Geoscience and Remote Sensing, 28, 711–720.
N.J. Nilsson, 1965: Learning Machines. N. Y., McGraw Hill.
Y. H. Pao, 1989: Adaptive Pattern Recognition and Neural Networks. Reading, Addison- Wesley.
S. Peleg and A. Rosenfeld, 1980: A New Probabilistic Relaxation Procedure. IEEE Trans. Pattern Analysis and Machine Intelligence, PAMI-2, 362–369.
T. L. Phillips (Ed.), 1973: Larsys Version 3 Users Manual, Laboratory for Applications of Remote Sensing, Purdue University, West Lafayette.
J. A. Richards, D. A. Landgrebe & P. H. Swain, 1981: On the Accuracy of Pixel Relaxation Labelling. IEEE Trans. Systems, Man and Cybernetics, SMC-11, 303–309.
J. A. Richards, D. A. Landgrebe & P. H. Swain, 1982: A Means for Utilizing Ancillary Information in Multispectral Classification. Remote Sensing of Environment, 12, 463–477.
A. Rosenfeld, R. Hummel and S. Zucker, 1976: Scene Labeling by Relaxation Algorithms. IEEE Trans Systems, Man and Cybernetics, SMC-6, 420–433.
G. Shafer, 1976: A Mathematical Theory of Evidence. NJ, Princeton UP.
A. H. Strahler, 1980: The Use of Prior Probabilities in Maximum Likelihood Classification of Remotely Sensed Data. Remote Sensing of Environment, 10, 135–163.
P. H. Swain and S. M. Davis (Eds.), 1978: Remote Sensing: The Quantitative Approach, N. Y., McGraw-Hill.
P.H. Swain and H. Hauska, 1977: The Decision Tree Classifier: Design and Potential. IEEE Trans. Geoscience Electronics, GE-15, 142–147.
P. H. Swain, S. B. Varderman and J. C. Tilton, 1981: Contextual Classification of Multispectral Image Data. Pattern Recognition, 13, 429–441.
J.T. Tou and R.C. Gonzalez, 1974: Pattern Recognition Principles, Mass., Addison Wesley.
E. Townsend, 1986: The Enhancement of Computer Classifications by Logical Smoothing. Photogrammetric Engineering and Remote Sensing, 52, 213–221.
A. G. Wacker and D. A. Landgrebe, 1972: Minimum Distance Classification in Remote Sensing. First Canadian Symp. on Remote Sensing, Ottawa.
S. D. Zenzo, R. Bernstein, S. D. Degloria and H. G. Kolsky, 1987: Gaussian Maximum Likelihood and Contextual Classification Algorithms for Multicrop Classification. IEEE Trans Geoscience and Remote Sensing, 25, 805–814.
S.D. Zenzo, S. D. Degloria, R. Bernstein and H.G. Kolsky, 1987: Gaussian Maximum Likelihood and Contextual Classification Algorithms for Multicrop Classification Experiments Using Thematic Mapper and Multispectral Scanner Sensor Data. IEEE Trans Geoscience and Remote Sensing, 25, 815–824.
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Richards, J.A. (1993). Supervised Classification Techniques. In: Remote Sensing Digital Image Analysis. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-88087-2_8
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DOI: https://doi.org/10.1007/978-3-642-88087-2_8
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