A lightweight and scalable greenhouse mapping method based on remote sensing imagery

Abstract

Seeking a low-cost, high-efficiency greenhouse mapping technology has immense significance. While greenhouse extraction methods using deep learning have been proposed, the challenge of extracting dense small objects remains an unresolved problem. The inherent downscaling strategy in general-purpose semantic segmentation (SS) models renders them unsuitable for such tasks. In contrast, the dramatically increasing computational complexity associated with this problem may result in an unaffordable cost for consumer-level applications. To address the aforementioned challenges, this study presents a novel greenhouse mapping model based on remote sensing (RS) images, which not only exhibits high precision and robust generalization capabilities but also offers significant lightweight advantages. To meet broader needs, we also provide corresponding customizable and scalable rules that allow for a trade-off between accuracy and speed. To evaluate the performance of our model, we select several representative works to conduct benchmark experiments on a self-annotated dataset. The results demonstrate that our method can provide more powerful visual representations for greenhouse segmentation with minimal cost. Compared to the control group, the proposed method achieves an mIoU improvement of 1.116 %-10.77 % using only 3.282 M parameters, while maintaining a considerable inference speed. Code will be available at: https://github.com/W-qp/EGENet.git

Keywords

• Greenhouses
• Deep learning
• Semantic segmentation
• Remote sensing imagery
• Low-cost