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  4. Vol 8, Issue 2, 2024

Study on Vegetation Extraction from Riparian Zone Images Based on Cswin Transformer

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DOI: 10.23977/acss.2024.080209 | Downloads: 8 | Views: 89

Author(s)

Yuanjie Ma 1, Yaping Zhang 1

Affiliation(s)

1 School of Information Science and Technology, Yunnan Normal University, Kunming, Yunnan, 650500, China

Corresponding Author

Yaping Zhang

ABSTRACT

In the field of ecological conservation, accurately extracting vegetation areas in UAV images is a critical task. This study aims to accurately identify vegetation from high-resolution riverine zone UAV images. Facing the challenges of complex factors such as light variations and water ripples, a deep learning technique, which combines Convolutional Neural Networks and Vision Transformer, is used in this study, which proposes a semantic segmentation network structure based on an encoder-decoder. We innovatively introduce the Explicit Visual Center mechanism (EVC) and CSWin Transformer structure to optimize image feature capture, especially in dealing with the classification challenges caused by the similarity between vegetation and water ripples. The experimental results show that the proposed network has the best results compared with the classical network models such as U-Net, PSP-Net, DeepLabv3+, etc., and the mIOU phase of U-Net, which is the highest among the three networks, is 1.3 percentage points higher. In this paper, an effective scheme is proposed for vegetation extraction from UAV images in the riparian zone.

KEYWORDS

Vegetation extraction; deep learning; semantic segmentation; Transformer

CITE THIS PAPER

Yuanjie Ma, Yaping Zhang, Study on Vegetation Extraction from Riparian Zone Images Based on Cswin Transformer. Advances in Computer, Signals and Systems (2024) Vol. 8: 57-62. DOI: http://dx.doi.org/10.23977/acss.2024.080209.

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