Segmentation is a basic and pivotal step of object-oriented remote sensing image classification

and the scale is a key problem of image segmentation. Aiming at the optimal segmentation scale selection for object-oriented remote sensing image classification

conformity degree between vectorial boundary lines of image region object after segmentation and true boundary lines of classification objects as criterion

through their multi-dimensions distance to define the conformity degree

the paper brought forward a new method of optimal segmentation scale selection for object-oriented remote sensing image classification-vector distance index method. Research verified the validity and applicability of this method through two experiments One experiment compared the results of optimal segmentation scale selection based on vector distance index method and ‘trial and error’ method. Results showed the vector distance index could reflect the optimal segmentation scale for seven classes exactly. The other experiment classified the segmentation result from the first experiment

through the accuracy assessment

explored the relationship of selection result based on vector distance index method and classification accuracy. Results showed water

dryland

rice

forest and resident region gained the highest accuracy on the scale that was selected by the vector distance index method

although marsh and grass didnt gain the highest accuracy on the scale that was selected by the vector distance index method

through further analysis

the classification result tallied with the practical condition. Then both two experiment results showed that this method could realize the optimal segmentation scale selection for object-oriented remote sensing image classification

which was similar to human thought

unconfined to data source

intuitionistic

comprehensible and practical. Based on the basic theory of vector distance index method

aiming at the ‘submergence’ and ‘fragmentation’ phenomenon

research brought forward a scale index

which could reflect the small or big status for a given object type

and provided a quantitative tool to assess the conflict degree between ‘submergence’ and ‘fragmentation’

then showed its significance during the process of segmentation scale selection.