Farm Detection based on Deep Convolutional Neural Nets and Semi-supervised Green Texture Detection using VIS-NIR Satellite Image

Sara Sharifzadeh, Jagati Tata, Bo Tan

    Research output: Chapter in Book/Report/Conference proceedingConference proceedingpeer-review

    3 Citations (Scopus)
    92 Downloads (Pure)

    Abstract

    Farm detection using low resolution satellite images is an important topic in digital agriculture. However, it has not received enough attention compared to high-resolution images. Although high resolution images are more efficient for detection of land cover components, the analysis of low-resolution images are yet important due to the low-resolution repositories of the past satellite images used for timeseries analysis, free availability and economic concerns. The current paper addresses the problem of farm detection using low resolution satellite images. In digital agriculture, farm detection has significant role for key applications such as crop yield monitoring. Two main categories of object detection strategies are studied and compared in this paper; First, a two-step semi-supervised methodology is developed using traditional manual feature extraction and modelling techniques; the developed methodology uses the Normalized Difference Moisture Index (NDMI), Grey Level Co-occurrence Matrix (GLCM), 2-D Discrete Cosine Transform (DCT) and morphological features and Support Vector Machine (SVM) for classifier modelling. In the second strategy, high-level features learnt from the massive filter banks of deep Convolutional Neural Networks (CNNs) are utilised. Transfer learning strategies are employed for pretrained Visual Geometry Group Network (VGG-16) networks. Results show the superiority of the high-level features for classification of farm regions.
    Original languageEnglish
    Title of host publicationDATA 2019 - Proceedings of the 8th International Conference on Data Science, Technology and Applications
    EditorsSlimane Hammoudi, Christoph Quix, Jorge Bernardino
    PublisherSciTePress
    Pages100-108
    Number of pages9
    Volume1
    ISBN (Electronic)9789897583773
    ISBN (Print)978-989-758-377-3
    DOIs
    Publication statusPublished - 2019
    Event8th International Conference on Data Science, Technology and Applications - VI ENNA HOUSE DIPLOMAT PRAGUE, Prague, Czech Republic
    Duration: 26 Jul 201928 Jul 2019
    Conference number: 8
    http://www.dataconference.org/?y=2019

    Publication series

    NameDATA 2019 - Proceedings of the 8th International Conference on Data Science, Technology and Applications

    Conference

    Conference8th International Conference on Data Science, Technology and Applications
    Abbreviated titleData2019
    Country/TerritoryCzech Republic
    CityPrague
    Period26/07/1928/07/19
    Internet address

    Keywords

    • Classification
    • Convolutional Neural Nets (CNNs)
    • Digital Agriculture
    • Satellite Image
    • Supervised Feature Extraction

    ASJC Scopus subject areas

    • Hardware and Architecture
    • Information Systems
    • Software
    • Computer Networks and Communications

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