Automatic counting of large mammals from very high resolution panchromatic satellite imagery

Yifei Xue*, Tiejun Wang, Andrew K. Skidmore

*Corresponding author for this work

    Research output: Contribution to journalArticle

    18 Citations (Scopus)
    13 Downloads (Pure)

    Abstract

    Estimating animal populations by direct counting is an essential component of wildlife conservation and management. However, conventional approaches (i.e., ground survey and aerial survey) have intrinsic constraints. Advances in image data capture and processing provide new opportunities for using applied remote sensing to count animals. Previous studies have demonstrated the feasibility of using very high resolution multispectral satellite images for animal detection, but to date, the practicality of detecting animals from space using panchromatic imagery has not been proven. This study demonstrates that it is possible to detect and count large mammals (e.g., wildebeests and zebras) from a single, very high resolution GeoEye-1 panchromatic image in open savanna. A novel semi-supervised object-based method that combines a wavelet algorithm and a fuzzy neural network was developed. To discern large mammals from their surroundings and discriminate between animals and non-targets, we used the wavelet technique to highlight potential objects. To make full use of geometric attributes, we carefully trained the classifier, using the adaptive-network-based fuzzy inference system. Our proposed method (with an accuracy index of 0.79) significantly outperformed the traditional threshold-based method (with an accuracy index of 0.58) detecting large mammals in open savanna.

    Original languageEnglish
    Article number878
    Pages (from-to)1-16
    Number of pages16
    JournalRemote Sensing
    Volume9
    Issue number9
    DOIs
    Publication statusPublished - 2017

    Bibliographical note

    Copyright the Author(s) 2017. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.

    Keywords

    • GeoEye-1
    • wavelet transform
    • fuzzy neural network
    • remote sensing
    • conservation

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