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Remote Sensing
Volume 17
Issue 12
10.3390/rs17122061
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Article

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

by
Yuguang Chang
1,
Xiaoyu Yu
2,3,
Xu Yang
3,4,
Zhengchao Chen
3,4,
Pan Chen
5,
Xuan Yang
6 and
Yongqing Bai
3,*
1
School of Civil Engineering, Henan Polytechnic University, Jiaozuo 454000, China
2
School of Resources and Environment, Henan Polytechnic University, Jiaozuo 454000, China
3
State Key Laboratory of Remote Sensing and Digital Earth, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
4
University of Chinese Academy of Sciences, Beijing 100049, China
5
Center for Geo-Spatial Information, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
6
China Remote Sensing Satellite Ground Station, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2025, 17(12), 2061; https://doi.org/10.3390/rs17122061
Submission received: 24 April 2025 / Revised: 12 June 2025 / Accepted: 12 June 2025 / Published: 15 June 2025
(This article belongs to the Special Issue Applications of Remote Sensing in Landscapes and Human Settlements)
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Abstract

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.
Keywords: plastic greenhouses; high-resolution remote sensing; semantic segmentation; Transformer decoder; agricultural monitoring plastic greenhouses; high-resolution remote sensing; semantic segmentation; Transformer decoder; agricultural monitoring
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MDPI and ACS Style

Chang, Y.; Yu, X.; Yang, X.; Chen, Z.; Chen, P.; Yang, X.; Bai, Y. Agricultural Greenhouse Extraction Based on Multi-Scale Feature Fusion and GF-2 Remote Sensing Imagery. Remote Sens. 2025, 17, 2061. https://doi.org/10.3390/rs17122061

AMA Style

Chang Y, Yu X, Yang X, Chen Z, Chen P, Yang X, Bai Y. Agricultural Greenhouse Extraction Based on Multi-Scale Feature Fusion and GF-2 Remote Sensing Imagery. Remote Sensing. 2025; 17(12):2061. https://doi.org/10.3390/rs17122061

Chicago/Turabian Style

Chang, Yuguang, Xiaoyu Yu, Xu Yang, Zhengchao Chen, Pan Chen, Xuan Yang, and Yongqing Bai. 2025. "Agricultural Greenhouse Extraction Based on Multi-Scale Feature Fusion and GF-2 Remote Sensing Imagery" Remote Sensing 17, no. 12: 2061. https://doi.org/10.3390/rs17122061

APA Style

Chang, Y., Yu, X., Yang, X., Chen, Z., Chen, P., Yang, X., & Bai, Y. (2025). Agricultural Greenhouse Extraction Based on Multi-Scale Feature Fusion and GF-2 Remote Sensing Imagery. Remote Sensing, 17(12), 2061. https://doi.org/10.3390/rs17122061

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