In order to experiment the performance of some popular ANN algorithms to OMIS (Operational Modular Imaging Spectrometer) hyperspectral image, three widely used ANNs, including Back Propagation Neural Network (BPNN), Radial Basis Function Neural Network (RBFNN), Fuzzy ARTMAP network and their improvements, are employed and compared. It is concluded that ANN classifiers perform much better than traditional classifiers such as SAM, MLC and MDC, and RBFNN outperforms BPNN and Fuzzy ARTMAP in terms of classification accuracy. It is also concluded that dimensionality reduction by PCA can be effectively used to feature extraction for hyperspectral image classification.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    ANN Classification of OMIS Hyperspectral Remotely Sensed Imagery: Experiments and Analysis


    Contributors:
    Du, Peijun (author) / Tan, Kun (author) / Zhang, Wei (author) / Yan, Zhigang (author)


    Publication date :

    2008-05-01


    Size :

    446363 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    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


    Knowledge-Based Road Extraction from High Resolution Remotely Sensed Imagery

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