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An improved semantic segmentation algorithm for high-resolution remote sensing images based on DeepLabv3.

Yan Wang^1,2, Ling Yang^3,4, Xinzhan Liu^1,2

¹College of Geography and Environmental Science, Henan University, Kaifeng, China.

Scientific Reports

|April 27, 2024

Summary

This summary is machine-generated.

This study introduces MST-DeepLabv3+, a novel model for remote sensing image semantic segmentation. It achieves high accuracy with fewer parameters by integrating MobileNetV2, SENet, and transfer learning, outperforming existing methods.

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Area of Science:

Computer Vision
Geospatial Analysis
Machine Learning

Background:

Semantic segmentation of high-resolution remote sensing images presents challenges in precision and efficiency.
Existing models often require extensive training data and possess numerous parameters, limiting their practical application.

Purpose of the Study:

To develop a novel semantic segmentation model, MST-DeepLabv3+, for remote sensing images.
To improve classification accuracy and efficiency while reducing model parameters compared to conventional approaches.

Main Methods:

The proposed MST-DeepLabv3+ model is based on DeepLabv3+.
Key modifications include replacing the Xception backbone with MobileNetV2, incorporating the Squeeze-and-Excitation Network (SENet) attention module, and enhancing transfer learning capabilities.

Main Results:

MST-DeepLabv3+ demonstrated superior performance on the International Society for Photogrammetry and Remote Sensing (ISPRS) and Gaofen Image Dataset (GID).
On the ISPRS dataset, mean intersection over union (MIoU) reached 82.47%, and overall accuracy (OA) was 92.13%.
Applied to the Taikang cultivated land dataset, MST-DeepLabv3+ achieved MIoU of 90.77% and OA of 95.47%, effectively segmenting edge information.

Conclusions:

MST-DeepLabv3+ significantly enhances semantic segmentation accuracy for remote sensing images.
The model effectively captures complete edge information and substantially reduces parameter size.
The integration of MobileNetV2, SENet, and transfer learning offers a more efficient and precise solution for remote sensing image analysis.