Abstract
Remote sensing imaging techniques make use of data derived from high resolution satellite sensors. Image classification identifies and organises pixels of similar spatial distribution or similar statistical characteristics into the same spectral class (theme). Contextual data can be incorporated, or ‘fused’, with spectral data to improve the accuracy of classification algorithms. In this paper we use Dempster–Shafer’s theory of evidence to achieve this data fusion. Incorporating a Knowledge Base of evidence within the classification process represents a new direction for the development of reliable systems for image classification and the interpretation of remotely sensed data.
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References
Anand S, Scotney B, Tan M, McClean S, Bell D, Hughes J and Magill I (1997). Designing a kernel for data mining. IEEE Expert 12: 65–74
Benediktsson J, Swain P and Ersoy O (1990). Neural network approaches versus statistical methods in classification of multisource remote sensing data. IEEE Trans Geosci Remote Sens 28(4): 540–552
Bourman C and Shapiro M (1994). A multiscale random field model for bayesian image segmentation. IEEE Trans Image Process 3(2): 162–177
Cheeseman P and Stutz J (1996). Bayesian classification: theory and results. In: Fayyad, U, Piatetsky, S, Smyth, P and Uthurusamy, R (eds) Advances in knowledge Discovery and Data Mining, pp 153–189. AAAI/MIT Press, Cambridge, MA
Congalton R (2001). Accuracy assessment and validation of remotely sensed and other spatial information. Int J Wildland Fire 10: 321–328
Debeir O, Latinne P and Van Den Steen I (2001). Remotes sensing classification of spectral, spatial and contextual data using multiple classifier systems. Image Anal Stereo 1(20): 584–589
Dempster A (1968). A generalisation of Bayesian inference. J R Stat Soc B 30: 205–247
Dengsheng L and Qihao W (2005). Urban classification using full spectral information of Landsat ETM + Imagery in Marion county, Indiana. P E & RS 71(11): 1275–1284
Deren LI, Kaichang DI, Deyi LI (2000) Land use classification of remote sensing image with GIS data based on spatial data mining techniques. Int Arch Photogrametric Remote Sens XXXIII (Part B3)
Dhodhi M, Saghri J, Ahmed I and Ul-Mustafa R (1999). D-ISODATA: a distributed algorithms for unsupervised classification of remotely sensed data on network of workstations. J Parallel Distrib Comput 59: 280–301
Duda T and Canty M (2002). Unsupervised classification of satellite imagery: choosing a good algorithm. Int J Remote Sens 23(11): 2193–2212
Egmont-Peterson M, De Ridder D and Handles H (2002). Image processing with neural networks—a review. Pattern Recognit 35: 2279–2301
Fangju W (1999). Fuzzy supervised classification of remotely sensing images. IEEE Trans Geosci Remote Sens 28(2): 194–201
Gibson P and Power C (2000). Introductory remote sensing, digital image processing applications. Taylor and Francis Group, Routledge
Gorte B and Stein A (1998). Bayesian classification and class area estimation of satellite images using stratification. IEEE Trans Geosci Remote Sens 36(3): 803–812
Guan J and Bell D (1991). Evidence theory and its applications. Elsevier Science Inc., New York
Guan J and Bell D (1992). Evidence theory and its applications. Elsevier Science Inc., New York
H’egarat-Mascle S, Richard D Le and Ottl’e C (2003). Multi-scale data fusion using Dempster–Shafer evidence theory. Integr Comput Aided Eng 10: 9–22
Huadong W, Siegel M, Stiefelhagen R and Jie Y (2002). Sensor fusion using Dempster–Shafer theory. Proc 19th IEEE Instrum Meas Technol Conf 1: 7–12
Jackson Q and Landgrebe D (2002). Adaptive Bayesian contextual classification based on Markov random fields. IEEE Trans Geosci Remote Sens 40(11): 2454–2463
Jensen J (1996). Introductory digital image Processing, a remote sensing prospective. Prentice Hall, Englewood cliffs
Jia X and Richards J (1994). Efficient maximum likelihood classification for image imaging spectrometer data sets. IEEE Trans Geosci Remote Sens 3(2): 274–281
Lillesand TM and Kiefer RW (2000). Remote sensing and image interpretation. John Wiley & Sons, New York
Mascle S, Bloch I and Vidal-Madjar D (1997). Application of Dempster–Shafer evidence theory to unsupervised classification in multisource remote sensing. IEEE Trans Geosci Remote Sens 35(4): 1018–1031
Mather P (1999). Computer processing of remotely-sensed images analysis, An Introduction,. John Wiley & Sons, Chichester
McClean S and Scotney B (1997). Using evidence theory for the integration of distributed databases. Int J Intell Syst 12: 763–776
Murai H and Omatsu S (1997). Remote sensing image analysis using neural and knowledge-based processing. Int J Remote Sens 18(4): 811–828
Richards J and Jia X (1999). Remote sensing digital image analysis an Introduction. Springer, New York
Shafer G (1982). Belief functions and parametric models. J R Stat Soc B 44: 322–352
Torsun I (1995). Foundation of intelligent Knowledge-based systems. Academic Press, San Diego
U.S. Geological Survey Web Site (2006) http://seamless.usgs.gov/. Cited 30 Nov 2006
Warrender C and Augusteijn M (1999). Fusion of image classification using Bayesian techniques with Markov random fields. Int J Remote Sens 20(10): 1987–2002
Engemann K, Filev D and Yager (1995). On the concept of immediate probabilities. Int J Intell Syst 10: 373–397
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Al Momani, B., McClean, S. & Morrow, P. Using Dempster–Shafer to incorporate knowledge into satellite image classification. Artif Intell Rev 25, 161–178 (2006). https://doi.org/10.1007/s10462-007-9027-4
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DOI: https://doi.org/10.1007/s10462-007-9027-4