基于UAV图像和SAM弱监督学习的黑土区保护性耕作玉米秸秆识别方法

Maize Straw Identification Method for Conservation Tillage in Black Soil Area Based on UAV Imagery and SAM Weak Supervision Learning

摘要:

秸秆覆盖还田是黑土区保护性耕作的重要手段，秸秆识别对于保护性耕作实施效果评估和农业管理决策具有重要意义。针对全监督深度学习秸秆遥感识别方法依赖大量像素级标注标签数据问题，提出一种基于无人机（UAV）图像和Segment anything model（SAM）的弱监督学习秸秆遥感识别方法。通过Adapter和联合损失函数对SAM进行微调，并利用边界框弱标注生成高质量伪标签，最终训练改进的U-Net分割网络实现秸秆识别。以吉林省梨树县玉米保护性耕作区为研究区进行秸秆提取试验，试验结果表明，微调后SAM的平均交并比和F1分数分别达到81.04%和87.85%，显著优于未微调模型；SAM弱监督结合改进U-Net的模型性能高于其他分割方法，F1分数为90.6%；消融试验验证了联合损失函数和卷积模块可有效提升模型性能。本文为黑土区玉米保护性耕作秸秆遥感识别提供了一种高效、低成本的解决方案。

Abstract:

Straw mulching is an important measure for conservation tillage in the black soil region. Straw identification is of great significance for the assessment of conservation tillage implementation effects and agricultural management decisions. To address the issue that fully supervised deep learning straw identification methods due to their reliance on a large amount of pixel-level labeled data, a weakly supervised learning straw identification method was proposed based on unmanned aerial vehicle (UAV) images and the segment anything model (SAM). By fine-tuned SAM with an adapter and a boundary-aware joint loss function, and generating high-quality pseudo-labels from bounding box weak annotations, an improved U-Net segmentation network was ultimately trained to achieve straw identification. A straw extraction experiment was conducted in the conservation tillage area of corn in Lishu County, Jilin Province as the research area. The results showed that the fine-tuned SAM achieved an MIoU of 81.04% and an F1-score of 87.85%, significantly outperforming the un-fine-tuned model. The model combining SAM weak supervision and the improved U-Net achieved a higher performance than other segmentation methods, with an F1-score of 90.6%. Ablation experiments verified the effectiveness of the joint loss function and convolutional modules in improving model performance. The research provided an efficient and cost-effective solution for remote sensing identification of straw identification in maize conservation tillage in the black soil region.