Greenhouse segmentation of remote sensing images based on deep learning

SONG Can; WU Jin; ZHU Lei; DENG Hui-ping

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Greenhouse segmentation of remote sensing images based on deep learning

更新时间：2026-09-04

- Greenhouse segmentation of remote sensing images based on deep learning
- Vol. 38, Issue 1, Pages: 51-56(2021)
- 作者机构：
  
  武汉科技大学机构信息科学与工程学院,湖北,武汉,430081
- 作者简介：
- 基金信息：
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- Published：2021
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宋灿, 吴谨, 朱磊, 邓慧萍. 针对遥感图像大棚提取的深度学习模型研究[J]. 微电子学与计算机, 2021, 38(1): 51-56.

SONG Can, WU Jin, ZHU Lei, et al. Greenhouse segmentation of remote sensing images based on deep learning[J]. 2021, 38(1): 51-56.
宋灿, 吴谨, 朱磊, 邓慧萍. 针对遥感图像大棚提取的深度学习模型研究[J]. 微电子学与计算机, 2021, 38(1): 51-56. DOI：

SONG Can, WU Jin, ZHU Lei, et al. Greenhouse segmentation of remote sensing images based on deep learning[J]. 2021, 38(1): 51-56. DOI：

摘要

大棚面积统计是蔬菜业进行科学管理的客观需求，通过深度学习自动获取大棚面积能更好地降低人工成本.为了准确获取大棚面积，本文使用基于深度学习的模型框架来解决这一问题.首先，使用金字塔池化模块和链式池化模块分别处理大尺度和小尺度的高层特征，在保持分辨率和避免退化的情况下提取多尺度的语义信息；然后，使用递归残差模块交替融合高层和低层信息，进一步优化分割结果。通过和三种较新的算法进行比较，实验结果表明本文的方法取得了最优的结果.

Abstract

Greenhouse area statistics is the objective requirement of scientific management of vegetable industry. It can reduce labor costs better to acquire greenhouse area automatically through deep learning. In order to obtain the greenhouse area accurately

a framework based on deep learning is proposed to solve this problem. First of all

pyramid pooling module and chain pooling module are used to deal with high-level features of large scale and small scale respectively

and multi-scale semantic information is extracted under the condition of maintaining resolution and avoiding degradation. Then

the recursive residual module is used to alternately fuse the high-level and low-level1 information to further optimize the segmentation results. Comparing with three newer algorithms

the experimental results show that the proposed method has achieved the best results..

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references

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