Abstract
This paper proposes a object-oriented change detection method based on the characteristic mapping pattern analysis.The method is improved from the information transfer model of remote sensing image interpretation.The image objects are acquired by the vector auxiliary data.The spectral and texture features are extracted,and an unsupervised clustering method is used to obtain the characteristic clusters of the objects.According to the priori information which exists in the auxiliary data,the mapping between the multi-temporal feature clusters is analyzed class by class.Then,the change object,whose mapping mode is inconsistent with other objects of the same class,can be identified.The experiments prove the feasibility and effectiveness of the proposed method,and the results show a new way for the object-oriented change detection.
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