AN EFFICIENT MULTI-SCALE SEGMENTATION FOR HIGH-RESOLUTION REMOTE SENSING IMAGERY BASED ON STATISTICAL REGION MERGING AND MINIMUM HETEROGENEITY RULE

摘要

Multi-scale segmentation is an essential step toward higher level image processing in remote sensing. This paper presents a new multi-scale segmentation method based on Statistical Region Merging (SRM) for initial segmentation and Minimum Heterogeneity Rule (MHR) for merging objects where high resolution (HR) QuickBird imageries are used. It synthesized the advantages of SRM and MHR. The SRM segmentation method not only considers spectral, shape, scale information, but also has the ability to cope with significant noise corruption, handle occlusions. The MHR used for merging objects takes advantages of its spectral, shape, scale information, and the local, global information. Compared with Fractal Net Evolution Approach (FNEA) eCognition adopted and SRM methods, the results showed that the proposed method overcame the disadvantages of them and was an effective multi-scale segmentation method for HR imagery.