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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

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Semi-Supervised Semantic Segmentation of Remote Sensing Images Based on Dual Cross-Entropy Consistency.

Mengtian Cui¹, Kai Li¹, Yulan Li¹

¹College of Computer Science and Engineering, Southwest Minzu University, Chengdu 610041, China.

Entropy (Basel, Switzerland)

|May 16, 2023

Summary

This summary is machine-generated.

This study introduces a semi-supervised semantic segmentation method for remote sensing images, reducing the need for labeled data. The novel approach enhances accuracy and efficiency by minimizing prediction entropy and utilizing a teacher-student structure.

Keywords:

channel attention mechanism cross-entropy consistency information entropy remote sensing image semi-supervised

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Area of Science:

Computer Vision
Machine Learning
Remote Sensing

Background:

Semantic segmentation in high-resolution remote sensing is complex.
Labeling remote sensing images is time-consuming and costly.
Existing methods often require extensive labeled data.

Purpose of the Study:

To develop a semi-supervised semantic segmentation method for remote sensing images.
To reduce the dependency on large amounts of labeled data.
To improve the efficiency and accuracy of remote sensing image analysis.

Main Methods:

Proposed a semi-supervised method using dual cross-entropy consistency and a teacher-student structure.
Incorporated a channel attention mechanism in the teacher model to reduce pseudo-label entropy.
Utilized a shared coding network and sharpening function in student models to ensure consistent and reduced information entropy.

Main Results:

The model effectively utilizes unlabeled remote sensing image data.
Achieved reduced prediction information entropy and required fewer labeled images.
Demonstrated superior performance compared to existing semi-supervised methods with half the labeled data.

Conclusions:

The proposed method significantly reduces the need for labeled data in remote sensing semantic segmentation.
It enhances the understanding of hidden information in unlabeled images.
Offers a more efficient and accurate solution for remote sensing image analysis.