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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

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:

benchmark crowdsourced data deep convolution neural network remote 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.