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Environmental Object Recognition in a Natural Image: An Experimental Approach Using Geographic Object-Based Image Analysis (GEOBIA)

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  • Jagannath Aryal

    (Discipline of Geography and Spatial Science, School of School of Land and Food, University of Tasmania, Hobart, Australia)

  • Didier Josselin

    (UMR ESPACE, University of Avignon, Avignon, France)

Abstract

Natural images, which are filled with intriguing stimuli of spatial objects, represent our cognition and are rich in spatial information. Accurate extraction of spatial objects is challenging due to the associated spatial and spectral complexities in object recognition. In this paper, the authors tackle the problem of spatial object extraction in a GEOgraphic Object Based Image Analysis framework taking psychological and mathematical complexities into account. In doing so, the authors experimented with human and GEOBIA based recognition and segmentation in an image of an area of natural importance, the Ventoux Mountain, France. Focus was given to scales, color, and texture properties at multiple levels in delineating the candidate spatial objects from the natural image. Such objects along with the original image were provided to the human subjects in two stages and three different groups of samples. The results of two stages were collated and analyzed. The analysis showed that there exist different ways to comprehend the geographical objects according to priori knowledge.

Suggested Citation

  • Jagannath Aryal & Didier Josselin, 2014. "Environmental Object Recognition in a Natural Image: An Experimental Approach Using Geographic Object-Based Image Analysis (GEOBIA)," International Journal of Agricultural and Environmental Information Systems (IJAEIS), IGI Global, vol. 5(1), pages 1-18, January.
    • Handle: RePEc:igg:jaeis0:v:5:y:2014:i:1:p:1-18
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    Cited by:

    1. Shu-Di Fan & Yue-Ming Hu & Lu Wang & Zhen-Hua Liu & Zhou Shi & Wen-Bin Wu & Yu-Chun Pan & Guang-Xing Wang & A-Xing Zhu & Bo Li, 2018. "Improving Spatial Soil Moisture Representation through the Integration of SMAP and PROBA-V Products," Sustainability, MDPI, vol. 10(10), pages 1-18, September.

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