Agricultural Greenhouse Extraction Based on Multi-Scale Feature Fusion and GF-2 Remote Sensing Imagery

Agricultural Greenhouse Extraction Based on Multi-Scale Feature Fusion and GF-2 Remote Sensing Imagery

Accurate extraction of plastic greenhouses from high-resolution remote sensing imagery is essential for agricultural resource management and facility-based crop monitoring. However, the dense spatial distribution, irregular morphology, and complex background interference of greenhouses often limit t...

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Bibliographic Details
Main Authors: Yuguang Chang, Xiaoyu Yu, Xu Yang, Zhengchao Chen, Pan Chen, Xuan Yang, Yongqing Bai
Format: Article
Language:English
Published: MDPI AG 2025-06-01
Series:Remote Sensing
Subjects:
plastic greenhouses
high-resolution remote sensing
semantic segmentation
Transformer decoder
agricultural monitoring
Online Access:https://www.mdpi.com/2072-4292/17/12/2061
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Summary:Accurate extraction of plastic greenhouses from high-resolution remote sensing imagery is essential for agricultural resource management and facility-based crop monitoring. However, the dense spatial distribution, irregular morphology, and complex background interference of greenhouses often limit the effectiveness of conventional segmentation methods. This study proposes a deep learning framework that integrates a multi-scale Transformer-based decoder with a Swin-UNet architecture to improve feature representation and extraction accuracy. To enhance geometric consistency, a post-processing strategy is introduced, combining connected component analysis and morphological operations to suppress noise and refine boundary shapes. Using GF-2 satellite imagery over Weifang City, China, the model achieved a recall of 92.44%, precision of 91.47%, intersection-over-union of 85.13%, and F1-score of 91.95%. In addition to instance-level extraction, spatial distribution and statistical analysis were performed across administrative divisions, revealing regional disparities in protected agriculture development. The proposed approach offers a practical solution for greenhouse mapping and supports broader applications in land use monitoring, agricultural policy enforcement, and resource inventory.
ISSN:2072-4292

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