Satellite image scene classification using spatial information

Weiwei Song, Dunwei Wen, Ke Wang, Tong Liu, Mujun Zang

    Research output: Chapter in Book/Report/Conference proceedingPublished Conference contributionpeer-review

    3 Citations (Scopus)

    Abstract

    In order to enhance the local feature's describing capacity and improve the classification performance of high-resolution (HR) satellite images, we present an HR satellite image scene classification method that make use of spatial information of local feature. First, the spatial pyramid matching model (SPMM) is adopted to encode spatial information of local feature. Then, images are represented by the local feature descriptors and encoding information. Finally, the support vector machine (SVM) classifier is employed to classify image scenes. The experiment results on a real satellite image dataset show that our method can classify the scene classes with an 82.6% accuracy, which indicates that the method can work well on describing HR satellite images and classifying different scenes.

    Original languageEnglish
    Title of host publicationSixth International Conference on Graphic and Image Processing, ICGIP 2014
    EditorsDavid Zhang, Yulin Wang, Xudong Jiang
    ISBN (Electronic)9781628415582
    DOIs
    Publication statusPublished - 2015
    Event6th International Conference on Graphic and Image Processing, ICGIP 2014 - Beijing, China
    Duration: 24 Oct. 201426 Oct. 2014

    Publication series

    NameProceedings of SPIE - The International Society for Optical Engineering
    Volume9443
    ISSN (Print)0277-786X
    ISSN (Electronic)1996-756X

    Conference

    Conference6th International Conference on Graphic and Image Processing, ICGIP 2014
    Country/TerritoryChina
    CityBeijing
    Period24/10/1426/10/14

    Keywords

    • Satellite image
    • spatial information
    • spatial pyramid model
    • support vector machine

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