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2013 | OriginalPaper | Chapter

Digital Image Processing: Post-processing and Data Integration

Authors : Prof. Siamak Khorram, Ph.D., Prof. Stacy Nelson, Ph.D., Dr. Halil Cakir, Ph.D., Dr. Cynthia Van Der Wiele, Ph.D.

Published in: Handbook of Satellite Applications

Publisher: Springer New York

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Abstract

The main objective of this chapter is to focus on the digital image processing, post-processing, and data integration techniques as applied to remotely sensed data for the purpose of extracting useful earth resources information. Image preprocessing and data reduction tools are described in the previous chapter. The concepts discussed in this chapter include:

Image processing techniques such as unsupervised image classifications, supervised image classifications, neural network classifiers, simulated annealing classifiers, and fuzzy logic classification systems
The most widely accepted indices and land use/land cover classification schemes
Post-processing techniques such as filtering and change detection
Accuracy assessment and validation of results
Data integration and spatial modeling including examples of integration of remotely sensed data with other conventional survey and map form data for Earth observation purposes

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S.G. Aaronoff, The minimum accuracy value as an index of classification accuracy. Photogrammetr. Eng. Remote Sens. 57(5), 501–509 (1985)

J.R. Anderson, E. Hardy, J. Roach, R. Witmer, A land use and land cover classification system for use with remote sensing data, US Geological Survey Professional Paper 964, Washington, DC, 1976, p. 28ff.

E.A. Blaisdell, Statistics in Practice (Harcourt Brace Javanovich, New York, 1993), p. 653ff

T. Celik, Unsupervised change detection in satellite images using principal component analysis and k-means clustering. Geoscien. Remote Sens. Lett. IEEE 6(4), 772–776 (2009)CrossRef

V. Cerny, Thermodynamical approach to the traveling salesman problem: an efficient simulation algorithm. J. Optim. Theory Appl. 45, 45–51 (1985)MathSciNet

S.B. Cho, J.H. Kim, Combining multiple neural networks by fuzzy integral for robust classification. IEEE Trans. Syst. Man Cybern. 25(2), 380–384 (1995)CrossRef

J.A. Cohen, A coefficient of agreement for nominal scales. Educ. Psychol. Meas. 20, 37–46 (1960)CrossRef

R.G. Congalton, K. Green, Assessing the Accuracy of Remotely Sensed Data: Principles and Practices (Lewis, Boca Raton, 1999), 137 p

R.G. Congalton, R.G. Oderwald, R.A. Mead, Assessing landsat classification accuracy using discrete multivariate statistical techniques. Photogrammetr. Eng. Remote Sens. 49(12), 1671–1678 (1983)

L.M. Cowardin, V. Carter, F.C. Golet, E.T. LaRoe, Classification of Wetlands and Deepwater Habitats of the United States (U.S. Fish and Wildlife Service, Washington, 1979), p. 103ff. FWS/OBS-79/31

X. Dai, S. Khorram, A new automated land cover change detection system for remotely-sensed imagery based on artifical neural networks, in Proceedings of the IEEE/IGARSS 1997 International Geoscience and Remote Sensing Symposium, Singapore, 1997

X. Dai, S. Khorram, Quantification of the impact of misregistration on digital change detection accuracy, in Proceedings of the IEEE/IGARSS 1997 International Geoscience and Remote Sensing Symposium, Singapore, 1997

X. Dai, S. Khorram, Data fusion using artificial neural networks: a case study on multitemporal change analysis. Comput. Environ. Urban Syst. 23, 19–31 (1999)CrossRef

A. Das, B.K. Chakrabarti, Quantum annealing and related optimization methods. Lect. Notes Phys. 679, 239–257 (2005)CrossRef

J. De Vincente, J. Lanchares, J. Hermida, Placement by thermodynamic simulated annealing. Phys. Lett. A 317, 415–423 (2003)CrossRef

J.R. Dobson, E.A. Bright, R.L. Ferguson, D.W. Field, L.L. Wood, K.D. Haddad, H. Iredale, J.R. Jensen, V. Klemas, R.J. Orth, J. P. Thomas, NOAA Coastal Change Analysis Program (C-CAP); Guidance for Regional Implementation, National Oceanic & Atmospheric Administration, Washington, NMFS 123, 1995, p. 92ff

A.S. Elfishawy, S.B. Kesler, Adaptive algorithms for change detection in image sequence. Signal Process. 23, 179–191 (1991)CrossRef

G.M. Foody, Status of land cover classification accuracy assessment. Remote Sens. Environ. 80, 185–201 (2002)CrossRef

M.F. Goodchild, G.Q. Sun, S. Yang, Development and test of an error model for categorical data. Int. J. Geogr. Inf. Syst. 6(2), 87–104 (1992)CrossRef

A. Hagen, Fuzzy set approach to assessing similarity of categorical maps. Int. J. Geogr. Inf. Sci. 17, 235–249 (2003)CrossRef

D.B. Hester, Land cover mapping and change detection in urban watersheds using quick bird high spatial resolution satellite imagery, Ph.D. Dissertation, North Carolina State University, 2008, p. 148

D.B. Hester, H.I. Cakir, S.A.C. Nelson, S. Khorram, Per-pixel classification of high spatial resolution satellite imagery for urban land cover mapping. Photogramm. Eng. Remote Sens. 74, 463–471 (2008)

D.B. Hester, S.A.C. Nelson, H.I. Cakir, S. Khorram, H. Cheshire, High resolution land cover change detection based on fuzzy uncertainty analysis and change reasoning. Int. J Remote Sens. 31, 455–475 (2010)CrossRef

R.M. Hord, Digital Image Processing of Remotely-Sensed Data (Academic, New York, 1982), p. 256

A.K. Jain, Fundamentals of Digital Image Processing (Prentice Hall, Englewood Cliffs, 1989), pp. 418–421MATH

J.R. Jensen, Introductory Digital Image Processing, Third edn. (Pearson Prentice Hall, Upper Saddle River, 2005). 316p

I. Kanellopoulos, G.G. Wilkinson, Strategies and best practice for neural network image classification. Int. J. Remote Sens. 18, 711–725 (1997)CrossRef

S. Khorram, H.M. Cheshire, K. Sidrellis, Z. Nagy. Mapping and GIS Development of Land Use/Land Cover Categories for the Albemarle-Pamlico Drainage Basin, NC Department of Environmental, Health, and Natural Resources, Dept. No. 91–08, 1992, p. 55ff

S. Khorram, H. Cheshire, X. Dai, J. Morisette, Land cover inventory and change detection of coastal North Carolina using landsat thematic mapper data. ASPRS/ACSM – Annu. Conv. Expos. 1, 245–250 (1996). Remote Sensing and Photogrammetry

S. Khorram, G.S. Biging, N.R. Chrisman, D.R. Colby, R.G. Congalton, J.E. Dobson, R.L. Ferguson, M.F. Goodchild, J.R. Jensen, T.H. Mace, Accuracy Assessment of Remote Sensing-Derived Change Detection. American Society of Photogrammetry and Remote Sensing, Monograph (1999)

S. Kirkpatrick, C.D. Gelatt Jr., M.P. Vecchi, Optimization by simulated annealing. Science 220, 671–688 (1983)MathSciNetMATH

V.V. Klemas, J.E. Dobson, R.L. Ferguson, K.D. Haddad, A coastal land cover classification system for the NOAA coastWatch change analysis program. J. Coast. Res. 9(3), 862–872 (1993)

T. Lillesand, R. Kiefer, J. Chipman, Remote Sensing and Image Interpretation, 6th edn. (Wiley, New York, 2008). 763p

R.L. Lunetta, J.G. Lyons (eds.), Geospatial data accuracy assessment. Report No. EPA/600/R-03/064 (US Environmental Protection Agency, Las Vegas, 2003), 335 p

J.T. Morisette, S. Khorram, Exact confidence interval with portions. Photogrammetr. Eng. Remote Sens. 5 (2007)

NOAA, Coastal Change Analysis Program (C-CAP), (Charleston: NOAA Coastal Services Center, 2004), http://www.csc.noaa-gov/crs/lca/ccap_program.html.

Y. Nogami, Y. Jyo, M. Yoshioka, S. Omatu, Remote sensing data analysis by Kohonen feature map and competitive learning. IEEE SMC’97 1, 524–529 (1997)

D.P. Paine, J.D. Kiser, Chapter 23: mapping accuracy assessment, in Aerial Photography and Image Interpretation, 2nd edn. (Wiley, New York, 2003), pp. 465–480

M. Pal, P.M. Mather, An assessment of the effectiveness of decision tree methods for land cover classification. Remote Sens. Environ. 86, 554–565 (2003)CrossRef

F. Qiu, J.R. Jensen, Opening of black box of neural networks for remote sensing image classification. Int. J. Remote Sens. 9, 1749–1768 (2004)CrossRef

D.E. Rumelhart, G.E. Hinton, R.J. Williams, Parallel Distributed Processing (MIT Press, Cambridge, MA, 1986)

K. Rutchey, L. Velcheck, Development of an everglades vegetation map using a SPOT image and global positioning system. Photogramm. Eng. Remote Sens. 60(6), 767–775 (1994)

M.J. Sabins, Convergence and consistency of fuzzy C-means/ISODATA algorithms. IEEE Trans. Pattern Anal. Mach. Intell. 9, 661–668 (1987)CrossRef

P.C. Shurr, Acceptance of the acceptance criteria for the simulated annealing algorithm. Math. Oper. Res. 22(2), 266ff (1997)

S.V. Stehman, Statistical rigor and practical utility in thematic map accuracy assessment. Photogrammetr. Eng. Remote Sens. 67, 727–734 (2001)

M. Story, R.G. Congalton, Accuracy assessment: a user’s perspective. Photogrammetr. Eng. Remote Sens. 52(3), 397–399 (1986)

J.T. Tou, R.C. Gonzalez, Pattern Recognition Principles (Addison-Wesley, Readings, 1977). 377p

J.L. Van Genderen, B.F. Lock, Testing land use map accuracy. Photogramm. Eng. Remote Sens. 43(9), 1135–1137 (1977)

USGS, USGS National Land Cover Data, (EROS Data Center, Sioux Falls, 2004), http://landcover.usgs.gov/prodescription.html.

F. Wang, Integrating GIS and remote sensing image analysis systems by unifying knowledge representation scheme. IEEE Trans. Geosci. Remote Sens. 29, 656–664 (1991)CrossRef

R. Xu, D. Wunsch, Survey of clustering algorithms. IEEE Trans. Neural Netw. 16, 32f (2005)CrossRef

C. Yang, P. Chung, Knowledge-based automatic change detection positioning system for complex heterogeneous environments. J. Intell. Robotic Syst. 33, 85–98 (2002)MATHCrossRef

Title: Digital Image Processing: Post-processing and Data Integration
Authors: Prof. Siamak Khorram, Ph.D.
Prof. Stacy Nelson, Ph.D.
Dr. Halil Cakir, Ph.D.
Dr. Cynthia Van Der Wiele, Ph.D.
Publisher: Springer New York
Book: Handbook of Satellite Applications
Print ISBN: 978-1-4419-7670-3

Electronic ISBN: 978-1-4419-7671-0

Copyright Year: 2013
DOI: https://doi.org/10.1007/978-1-4419-7671-0_92

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