Irrera gold2010

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INTEGRATION OF SUPPORT VECTOR MACHINES AND MARKOV RANDOM FIELDS FOR REMOTE SENSING IMAGE CLASSIFICATION Paolo Irrera, Gabriele Moser, Sebastiano B. Serpico University of Genoa, Dept. of Biophysical and Electronic Eng. (DIBE), Via Opera Pia 11a, I-16145 Genoa Italy

OUTLINE ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

REMOTE SENSING IMAGE CLASSIFICATION ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

OBJECTIVE OF THE PAPER ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

SVM CLASSIFIER ,[object Object],[object Object],[object Object],[object Object],Quadratic programming problem Discriminant function, nonlinear case, two classes

MARKOV RANDOM FIELDS ,[object Object],[object Object],[object Object]

INTEGRATING MRF AND SVM ,[object Object],[object Object],[object Object],[object Object],Discriminant function. Contextual kernel Kernel-based expression of the discriminant function

PROPOSED CLASSIFIER I = image n channels to be classified. T = training map. m = update classification map at each iteration.

PARAMETER OPTIMIZATION ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

DATA SETS FOR EXPERIMENTS ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

EXPERIMENTAL RESULTS CONFUSION MATRICES AND ACCURACIES Pavia. Confusion matrix, noncontextual SVM. Pavia. Confusion matrix, proposed method . Tanaro. Confusion matrix, noncontextual SVM . Tanaro. Confusion matrix, proposed method .

EXPERIMENTAL RESULTS CLASSIFICATION MAPS Pavia: map generated by a noncontextual SVM. Pavia: map generated by the proposed method. Tanaro: map generated by a noncontextual SVM. Tanaro : map generated by the proposed method.

EXPERIMENTAL RESULTS CONVERGENCE OF THE METHOD Tanaro: behavior of the accuracy ( overall accuracy – OA, average accuracy – AA, and crossvalidation accuracy – XVAL) as a function of the number of iterations of the proposed method.

CONCLUSIONS ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

REFERENCES ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

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Irrera gold2010

1. INTEGRATION OF SUPPORT VECTOR MACHINES AND MARKOV RANDOM FIELDS FOR REMOTE SENSING IMAGE CLASSIFICATION Paolo Irrera, Gabriele Moser, Sebastiano B. Serpico University of Genoa, Dept. of Biophysical and Electronic Eng. (DIBE), Via Opera Pia 11a, I-16145 Genoa Italy

8. PROPOSED CLASSIFIER I = image n channels to be classified. T = training map. m = update classification map at each iteration.

10.

11. EXPERIMENTAL RESULTS CONFUSION MATRICES AND ACCURACIES Pavia. Confusion matrix, noncontextual SVM. Pavia. Confusion matrix, proposed method . Tanaro. Confusion matrix, noncontextual SVM . Tanaro. Confusion matrix, proposed method .

12. EXPERIMENTAL RESULTS CLASSIFICATION MAPS Pavia: map generated by a noncontextual SVM. Pavia: map generated by the proposed method. Tanaro: map generated by a noncontextual SVM. Tanaro : map generated by the proposed method.

13. EXPERIMENTAL RESULTS CONVERGENCE OF THE METHOD Tanaro: behavior of the accuracy ( overall accuracy – OA, average accuracy – AA, and crossvalidation accuracy – XVAL) as a function of the number of iterations of the proposed method.

14.

15.