Image segmentation is often regarded as the first and most important step for other higher level image interpretation, e.g. information extraction and image mining. Although a lot of researches have dedicated to this field, due to its intrinsic dilemma, there exist a wide range of shortcomings of current segmentation methods. When applied to remotely sensed imagery which are commonly with tremendous data volume and very complex ground feature distributions, it will encounter much more difficulties in extracting meaningful and valuable patterns. In this research, we classify remotely sensed imagery into two types: the gray value and texture imagery, and then search their respective suitable segmentation methods. More than 12 segmentation algorithms are implemented and integrated into a multi-scale segmentation framework, which is illustrated and validated with two typical applications on segmenting and extracting manmade objects from high spatial resolution remotely sensed imagery.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Integration of Image Segmentation Methods for Information Extraction from Remotely Sensed Imagery


    Contributors:
    Wang, Min (author)


    Publication date :

    2008-05-01


    Size :

    445235 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Knowledge-Based Road Extraction from High Resolution Remotely Sensed Imagery

    Shen, Jing / Lin, Xiangguo / Shi, Yunfei et al. | IEEE | 2008


    Digital Processing of Remotely Sensed Imagery

    Green, William B. | NTRS | 1997


    Enabling Knowledge on Demand from Remotely Sensed Imagery

    Cromp, R. / IEEE; Aerospace and Electronics Systems Society | British Library Conference Proceedings | 1998


    Removal of the atmospheric blurring from remotely sensed earth imagery

    Kusaka, T. / Haba, Y. / Kawata, Y. et al. | Tema Archive | 1978