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RSI-CB: A Large-Scale Remote Sensing Image Classification Benchmark Using Crowdsourced Data.

Haifeng Li1, Xin Dou1, Chao Tao1

  • 1School of Geosciences and Info-Physics, Central South University, Changsha 410083, China.

Sensors (Basel, Switzerland)
|March 18, 2020
PubMed
Summary
This summary is machine-generated.

Researchers developed a new remote sensing image classification benchmark (RSI-CB) using crowdsourced data. This large-scale dataset is ideal for training deep convolutional neural networks (DCNNs) in the big data era.

Keywords:
benchmarkcrowdsourced datadeep convolution neural networkremote sensing image classification

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Area of Science:

  • Remote Sensing
  • Computer Vision
  • Machine Learning

Background:

  • Deep convolutional neural networks (DCNNs) excel at natural image recognition.
  • The remote sensing field lacks a large-scale benchmark comparable to ImageNet.
  • Existing benchmarks may not be sufficient for the demands of the big data era.

Purpose of the Study:

  • To propose and construct a novel, large-scale benchmark for remote sensing image classification.
  • To leverage crowdsourced data for effective annotation of remote sensing imagery.
  • To provide a robust dataset for training and evaluating DCNNs in remote sensing.

Main Methods:

  • Utilized crowdsourced data, including Open Street Map (OSM), for annotating remote sensing images.
  • Developed a worldwide, large-scale benchmark (RSI-CB) with significant geographical distribution.
  • Established a classification system with six categories and 35 sub-classes, inspired by ImageNet's hierarchy.

Main Results:

  • Constructed a benchmark containing over 24,000 images (256x256 pixels).
  • Demonstrated RSI-CB's suitability as a benchmark through comparative experiments.
  • RSI-CB proved more effective than SAT-4, SAT-6, and UC-Merced datasets for current remote sensing tasks.

Conclusions:

  • RSI-CB is a valuable resource for advancing remote sensing image classification.
  • The benchmark's scale and diversity make it suitable for the big data era.
  • RSI-CB has broad potential applications in various remote sensing domains.