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Related Experiment Video

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Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation
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Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

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A patch-based convolutional neural network for remote sensing image classification.

Atharva Sharma1, Xiuwen Liu1, Xiaojun Yang2

  • 1Department of Computer Science, Florida State University, Tallahassee, FL 32306-4530, United States.

Neural Networks : the Official Journal of the International Neural Network Society
|August 27, 2017
PubMed
Summary
This summary is machine-generated.

Accurate land cover mapping is crucial for environmental sustainability. A new deep convolutional neural network (CNN) system using patch-based samples significantly improves classification accuracy for medium-resolution remote sensing data.

Keywords:
CNNDeep learningMedium-resolutionPatch-basedRemote sensing imagerySpatial context

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Area of Science:

  • Remote Sensing
  • Geospatial Analysis
  • Environmental Monitoring

Background:

  • Accurate land cover information is vital for global environmental sustainability.
  • Existing per-pixel classification methods for medium-resolution remote sensing data suffer from low accuracy.
  • Deep convolutional neural networks (CNNs) excel in object recognition but struggle with medium-resolution data lacking fine structures.

Purpose of the Study:

  • To develop a novel deep patch-based CNN system for improved land cover classification using medium-resolution remote sensing data.
  • To address the limitations of per-pixel classification methods in medium-resolution imagery.

Main Methods:

  • A new deep patch-based CNN system was designed, considering the spatial relationship between pixels and their neighborhoods.
  • The system computes patch-based samples from multidimensional top-of-atmosphere reflectance data.
  • The proposed system was tested on a site in the Florida Everglades.

Main Results:

  • The proposed deep patch-based CNN system achieved superior performance compared to pixel-based neural networks, pixel-based CNNs, and patch-based neural networks.
  • Overall classification accuracy improvements were 24.36% over pixel-based neural networks, 24.23% over pixel-based CNNs, and 11.52% over patch-based neural networks.
  • The system demonstrated significant enhancements in classifying medium-resolution remote sensing data.

Conclusions:

  • The developed deep patch-based CNN system offers a more effective approach for land cover classification with medium-resolution remote sensing data.
  • Combining this CNN with extensive medium-resolution remote sensing data can lead to the creation of highly accurate, large-area land cover datasets.
  • This advancement supports better environmental monitoring and sustainability efforts.