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A Semantic Segmentation Method for Remote Sensing Images Based on an Improved TransDeepLab Model

Author

Listed:
  • Jinxin Wang

    (School of Geoscience & Technology, Zhengzhou University, Zhengzhou 450001, China)

  • Manman Wang

    (School of Geoscience & Technology, Zhengzhou University, Zhengzhou 450001, China)

  • Kaiwei Cong

    (School of Computer and Information Technology, Northeast Petroleum University, Daqing 163000, China)

  • Zilong Qin

    (School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China)

Abstract

Due to the various types of land cover and large spectral differences in remote sensing images, high-quality semantic segmentation of these images still faces challenges such as fuzzy object boundary extraction and difficulty in identifying small targets. To address these challenges, this study proposes a new improved model based on the TransDeepLab segmentation method. The model introduces a GAM attention mechanism in the coding stage, and incorporates a multi-level linear up-sampling strategy in the decoding stage. These enhancements allow the model to fully utilize multi-level semantic information and small target details in high-resolution remote sensing images, thereby effectively improving the segmentation accuracy of target objects. Using the open-source LoveDA large remote sensing image datasets for the validation experiment, the results show that compared to the original model, the improved model’s MIOU increased by 2.68%, aACC by 3.41%, and mACC by 4.65%. Compared to other mainstream models, the model also achieved superior segmentation performance.

Suggested Citation

  • Jinxin Wang & Manman Wang & Kaiwei Cong & Zilong Qin, 2024. "A Semantic Segmentation Method for Remote Sensing Images Based on an Improved TransDeepLab Model," Land, MDPI, vol. 14(1), pages 1-17, December.
    • Handle: RePEc:gam:jlands:v:14:y:2024:i:1:p:22-:d:1553817
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    References listed on IDEAS

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    1. Yunyan Wang & Chongyang Wang & Huaxuan Wu & Peng Chen, 2022. "An improved Deeplabv3+ semantic segmentation algorithm with multiple loss constraints," PLOS ONE, Public Library of Science, vol. 17(1), pages 1-14, January.
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