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Image segmentation and discriminant analysis for the identification of land cover units in ecology

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1 Author(s)
A. Lobo ; CSIS, Barcelona, Spain

The textured nature of most natural land cover units as represented in remotely sensed imagery causes limited results of per-pixel classifications. The segmentation algorithm, iterative mutually optimum region merging (IMORM), is presented and used to partition images into elements that are thereafter classified by linear canonical discriminant analysis and a maximum likelihood allocation rule. This per-segment approach results in much higher accuracy than the conventional per-pixel approach. Furthermore, separability matrices indicate that many land cover categories cannot be correctly defined by per-pixel statistics

Published in:

IEEE Transactions on Geoscience and Remote Sensing  (Volume:35 ,  Issue: 5 )