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Farm detection based on deep convolutional neural nets and semi-supervised green texture detection using VIS-NIR satellite image

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

Details

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
ISBN (Electronic)9789897583773
DOIs
Publication statusPublished - 2019
Publication typeA4 Article in a conference publication
EventInternational Conference on Data Science, Technology and Applications - Prague, Czech Republic
Duration: 26 Jul 201928 Jul 2019

Conference

ConferenceInternational Conference on Data Science, Technology and Applications
CountryCzech Republic
CityPrague
Period26/07/1928/07/19

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.

Keywords

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

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