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3D Convolutional Neural Networks for Crop Classification with Multi-Temporal Remote Sensing Images

Remote sensing (Basel, Switzerland), 2018-01, Vol.10 (2), p.75 [Tạp chí có phản biện]

ISSN: 2072-4292 ;EISSN: 2072-4292 ;DOI: 10.3390/rs10010075

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  • Nhan đề:
    3D Convolutional Neural Networks for Crop Classification with Multi-Temporal Remote Sensing Images
  • Tác giả: Ji, Shunping ; Zhang, Chi ; Xu, Anjian ; Shi, Yun ; Duan, Yulin
  • Chủ đề: 3D convolution ; active learning ; convolutional neural networks ; crop classification ; multi-temporal remote sensing images
  • Là 1 phần của: Remote sensing (Basel, Switzerland), 2018-01, Vol.10 (2), p.75
  • Mô tả: This study describes a novel three-dimensional (3D) convolutional neural networks (CNN) based method that automatically classifies crops from spatio-temporal remote sensing images. First, 3D kernel is designed according to the structure of multi-spectral multi-temporal remote sensing data. Secondly, the 3D CNN framework with fine-tuned parameters is designed for training 3D crop samples and learning spatio-temporal discriminative representations, with the full crop growth cycles being preserved. In addition, we introduce an active learning strategy to the CNN model to improve labelling accuracy up to a required threshold with the most efficiency. Finally, experiments are carried out to test the advantage of the 3D CNN, in comparison to the two-dimensional (2D) CNN and other conventional methods. Our experiments show that the 3D CNN is especially suitable in characterizing the dynamics of crop growth and outperformed the other mainstream methods.
  • Nơi xuất bản: MDPI AG
  • Ngôn ngữ: English
  • Số nhận dạng: ISSN: 2072-4292
    EISSN: 2072-4292
    DOI: 10.3390/rs10010075
  • Nguồn: Directory of Open Access Journals
    ROAD: Directory of Open Access Scholarly Resources
    ProQuest Central
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