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TMNet: A Two-Branch Multi-Scale Semantic Segmentation Network for Remote Sensing Images.

Yupeng Gao1,2, Shengwei Zhang3,4, Dongshi Zuo1,2

  • 1School of Computer and Information Engineering, Inner Mongolia Agricultural University, Hohhot 010011, China.

Sensors (Basel, Switzerland)
|July 14, 2023
PubMed
Summary

This study introduces TMNet, a novel deep learning network for remote sensing image semantic segmentation. TMNet enhances global feature extraction using Swin Transformers, improving accuracy in pixel-level analysis.

Keywords:
Swin transformerglobal modelingremote sensing imagessemantic segmentation

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Area of Science:

  • Computer Vision
  • Remote Sensing
  • Artificial Intelligence

Background:

  • Pixel-level information in remote sensing images is crucial for various applications.
  • Convolutional Neural Networks (CNNs) struggle with global feature extraction and contextual understanding in semantic segmentation.
  • Pure CNN models often yield suboptimal precision in remote sensing image analysis.

Purpose of the Study:

  • To design a novel two-branch multi-scale semantic segmentation network (TMNet) for remote sensing images.
  • To enhance the extraction of global feature information and contextual semantic interactions.
  • To improve the precision of semantic segmentation in remote sensing applications.

Main Methods:

  • A two-branch encoder-decoder network architecture was developed.
  • Swin Transformer was integrated to improve global feature coding capabilities.
  • A multi-scale feature fusion module (MFM), feature enhancement module (FEM), and channel enhancement module (CEM) were designed and incorporated.

Main Results:

  • TMNet demonstrated excellent performance on the WHDLD and Potsdam datasets.
  • The integration of Swin Transformer improved global feature extraction.
  • The proposed MFM, FEM, and CEM modules effectively enhanced feature extraction and semantic segmentation accuracy.

Conclusions:

  • TMNet offers a significant advancement in semantic segmentation for remote sensing images.
  • The network effectively combines local and global feature extraction for improved accuracy.
  • The proposed architectural components contribute to superior performance in analyzing remote sensing data.