Seamline determination based on semantic segmentation for aerial image mosaicking

Shunta Saito, Ryota Arai, Yoshimitsu Aoki

    Research output: Contribution to journalArticle

    9 Citations (Scopus)

    Abstract

    We propose a novel method for seamline determination based on semantic segmentation for aerial image mosaicking. First, we train a convolutional neural network (CNN) for pixel labeling to extract building regions. Using the trained CNN, we create a building probability map from an input aerial image with no pre-processing. We then use Dijkstra's algorithm to find the optimal seamline as a shortest path on the map. We evaluate the quality of the seamlines produced by our method on actual aerial images. Finally, we show that our seamlines never pass through any buildings and compare the effectiveness with the conventional mean-shift segmentation-based method.

    Original languageEnglish
    Article number7355281
    Pages (from-to)2847-2856
    Number of pages10
    JournalIEEE Access
    Volume3
    DOIs
    Publication statusPublished - 2015 Dec 17

    Keywords

    • Artificial neural networks
    • Computer vision
    • Image processing
    • Neural networks
    • Remote sensing

    ASJC Scopus subject areas

    • Computer Science(all)
    • Materials Science(all)
    • Engineering(all)

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