Abstract
Image segmentation is an essential step toward higher level image processing in remote sensing. However, the traditional image segmentation approaches based on pixels spectral characteristics and single-scale image information extraction methods have obvious flaws in this respect. Currently, multi-scale image segmentation is seen as a promising alternative of traditional segmentation method and is one of the most useful approaches in object oriented classification of remotely sensed images. In this paper, we present a multi-scale segmentation method based on Minimum Heterogeneity Rule (MHR) for merging objects. Segmentation results show that this method can easily adapt its scale parameter to different scale image analysis tasks and any chosen scale object-extraction of interest.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
