Implementation of a Segmentation Method for Agricultural Fields in Aerial Sequences of Images Based on CSAR Model

H. Chen, Z. Houkes

    Research output: Chapter in Book/Report/Conference proceedingChapterAcademic

    Abstract

    In this paper, a segmentation method for agricultural fields in aerial sequences of images based on the Circular Symmetri Auto-Regressive (CSAR) model is presented. The image sequences assumed to be acquired by a video camera (RGB-CCD system) from an aeroplane, which moves linearly over the scene. The objects in the scenes being considered in this paper, are agricultural fields. The classes of agricultural fields to be distinguished are determined by the type of crop, e.g. potatoes sugar beet, wheat, etc. In order to recognize and classify these fields from aerial sequence of images, a reliable segmentatio is required. Here texture features are used for segmentation. The implementation of segmentation for agricultural fields in aerial sequences of images is based on CSAR model in texture analysis. By comparing the estimated parameters of CSAR model from different area in an image, the characteristics and the class of a texture may be determined. The paper describes the segmentation method and its evaluation through experiments. Based on segmentation results, classification for surface texture of vegetation from aerial sequences of images is realized.
    Original languageEnglish
    Title of host publicationElectronic imaging
    Subtitle of host publicationprocessing, printing, and publishing in color : 18-20 May 1998, Zurich, Switzerland
    EditorsJan Bareš
    Place of PublicationBellingham, WA
    PublisherSPIE
    Pages368-376
    Number of pages9
    ISBN (Print)9780819428615
    DOIs
    Publication statusPublished - 1998
    EventSYBEN-Broadband European Networks and Electronic Image Capture and Publishing 1998 - Zurich, Switzerland
    Duration: 18 May 199822 May 1998

    Publication series

    NameProceedings of SPIE
    PublisherSPIE
    Volume3409
    ISSN (Print)0277-786X
    ISSN (Electronic)1996-756X

    Conference

    ConferenceSYBEN-Broadband European Networks and Electronic Image Capture and Publishing 1998
    CountrySwitzerland
    CityZurich
    Period18/05/9822/05/98

    Fingerprint

    segmentation
    texture
    sugar beet
    potato
    method
    wheat
    crop
    vegetation
    experiment

    Keywords

    • METIS-112569

    Cite this

    Chen, H., & Houkes, Z. (1998). Implementation of a Segmentation Method for Agricultural Fields in Aerial Sequences of Images Based on CSAR Model. In J. Bareš (Ed.), Electronic imaging: processing, printing, and publishing in color : 18-20 May 1998, Zurich, Switzerland (pp. 368-376). (Proceedings of SPIE; Vol. 3409). Bellingham, WA: SPIE. https://doi.org/10.1117/12.324129
    Chen, H. ; Houkes, Z. / Implementation of a Segmentation Method for Agricultural Fields in Aerial Sequences of Images Based on CSAR Model. Electronic imaging: processing, printing, and publishing in color : 18-20 May 1998, Zurich, Switzerland. editor / Jan Bareš. Bellingham, WA : SPIE, 1998. pp. 368-376 (Proceedings of SPIE).
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    title = "Implementation of a Segmentation Method for Agricultural Fields in Aerial Sequences of Images Based on CSAR Model",
    abstract = "In this paper, a segmentation method for agricultural fields in aerial sequences of images based on the Circular Symmetri Auto-Regressive (CSAR) model is presented. The image sequences assumed to be acquired by a video camera (RGB-CCD system) from an aeroplane, which moves linearly over the scene. The objects in the scenes being considered in this paper, are agricultural fields. The classes of agricultural fields to be distinguished are determined by the type of crop, e.g. potatoes sugar beet, wheat, etc. In order to recognize and classify these fields from aerial sequence of images, a reliable segmentatio is required. Here texture features are used for segmentation. The implementation of segmentation for agricultural fields in aerial sequences of images is based on CSAR model in texture analysis. By comparing the estimated parameters of CSAR model from different area in an image, the characteristics and the class of a texture may be determined. The paper describes the segmentation method and its evaluation through experiments. Based on segmentation results, classification for surface texture of vegetation from aerial sequences of images is realized.",
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    author = "H. Chen and Z. Houkes",
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    Chen, H & Houkes, Z 1998, Implementation of a Segmentation Method for Agricultural Fields in Aerial Sequences of Images Based on CSAR Model. in J Bareš (ed.), Electronic imaging: processing, printing, and publishing in color : 18-20 May 1998, Zurich, Switzerland. Proceedings of SPIE, vol. 3409, SPIE, Bellingham, WA, pp. 368-376, SYBEN-Broadband European Networks and Electronic Image Capture and Publishing 1998, Zurich, Switzerland, 18/05/98. https://doi.org/10.1117/12.324129

    Implementation of a Segmentation Method for Agricultural Fields in Aerial Sequences of Images Based on CSAR Model. / Chen, H.; Houkes, Z.

    Electronic imaging: processing, printing, and publishing in color : 18-20 May 1998, Zurich, Switzerland. ed. / Jan Bareš. Bellingham, WA : SPIE, 1998. p. 368-376 (Proceedings of SPIE; Vol. 3409).

    Research output: Chapter in Book/Report/Conference proceedingChapterAcademic

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    T1 - Implementation of a Segmentation Method for Agricultural Fields in Aerial Sequences of Images Based on CSAR Model

    AU - Chen, H.

    AU - Houkes, Z.

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    N2 - In this paper, a segmentation method for agricultural fields in aerial sequences of images based on the Circular Symmetri Auto-Regressive (CSAR) model is presented. The image sequences assumed to be acquired by a video camera (RGB-CCD system) from an aeroplane, which moves linearly over the scene. The objects in the scenes being considered in this paper, are agricultural fields. The classes of agricultural fields to be distinguished are determined by the type of crop, e.g. potatoes sugar beet, wheat, etc. In order to recognize and classify these fields from aerial sequence of images, a reliable segmentatio is required. Here texture features are used for segmentation. The implementation of segmentation for agricultural fields in aerial sequences of images is based on CSAR model in texture analysis. By comparing the estimated parameters of CSAR model from different area in an image, the characteristics and the class of a texture may be determined. The paper describes the segmentation method and its evaluation through experiments. Based on segmentation results, classification for surface texture of vegetation from aerial sequences of images is realized.

    AB - In this paper, a segmentation method for agricultural fields in aerial sequences of images based on the Circular Symmetri Auto-Regressive (CSAR) model is presented. The image sequences assumed to be acquired by a video camera (RGB-CCD system) from an aeroplane, which moves linearly over the scene. The objects in the scenes being considered in this paper, are agricultural fields. The classes of agricultural fields to be distinguished are determined by the type of crop, e.g. potatoes sugar beet, wheat, etc. In order to recognize and classify these fields from aerial sequence of images, a reliable segmentatio is required. Here texture features are used for segmentation. The implementation of segmentation for agricultural fields in aerial sequences of images is based on CSAR model in texture analysis. By comparing the estimated parameters of CSAR model from different area in an image, the characteristics and the class of a texture may be determined. The paper describes the segmentation method and its evaluation through experiments. Based on segmentation results, classification for surface texture of vegetation from aerial sequences of images is realized.

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    Chen H, Houkes Z. Implementation of a Segmentation Method for Agricultural Fields in Aerial Sequences of Images Based on CSAR Model. In Bareš J, editor, Electronic imaging: processing, printing, and publishing in color : 18-20 May 1998, Zurich, Switzerland. Bellingham, WA: SPIE. 1998. p. 368-376. (Proceedings of SPIE). https://doi.org/10.1117/12.324129