The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLIV-M-2-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIV-M-2-2020, 51–54, 2020
https://doi.org/10.5194/isprs-archives-XLIV-M-2-2020-51-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIV-M-2-2020, 51–54, 2020
https://doi.org/10.5194/isprs-archives-XLIV-M-2-2020-51-2020

  17 Nov 2020

17 Nov 2020

DEEP LEARNING FOR REMOTE SENSING IMAGE CLASSIFICATION FOR AGRICULTURE APPLICATIONS

L. Hashemi-Beni1 and A. Gebrehiwot2 L. Hashemi-Beni and A. Gebrehiwot
  • 1Geomatics Program, Department of Built Environment, North Carolina A&T State University, USA
  • 2Applied Science and Technology Ph.D. Program, Department of Built Environment, North Carolina A&T State University, USA

Keywords: Precision Agriculture, Remote Sensing, U-Net, FCN-8s, Deep Learning, Image Segmentation

Abstract. This research examines the ability of deep learning methods for remote sensing image classification for agriculture applications. U-net and convolutional neural networks are fine-tuned, utilized and tested for crop/weed classification. The dataset for this study includes 60 top-down images of an organic carrots field, which was collected by an autonomous vehicle and labeled by experts. FCN-8s model achieved 75.1% accuracy on detecting weeds compared to 66.72% of U-net using 60 training images. However, the U-net model performed better on detecting crops which is 60.48% compared to 47.86% of FCN-8s.