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Comparison of scene segmentations: SMAP, ECHO, and maximum likelihood

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2 Author(s)
McCauley, J.D. ; Dept. of Agric. Eng., Purdue Univ., West Lafayette, IN, USA ; Engel, B.A.

Sequential maximum a posteriori (SMAP) and the extraction and classification of homogeneous objects (ECHO), two spectral/spatial scene segmentation algorithms, were compared with traditional maximum likelihood (ML) estimation in a supervised classification of multispectral data. SMAP generalized better than both ECHO and ML. Significant differences were found in all mean class classification accuracies: SMAP>ECHO>ML

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Geoscience and Remote Sensing, IEEE Transactions on  (Volume:33 ,  Issue: 6 )