南方农业学报2025，Vol.56Issue(1)：111-123,13.DOI:10.3969/j.issn.2095-1191.2025.01.010
基于IRCF模型的无人机低空遥感影像田块边界提取方法研究
IRCF-based unmanned aerial vehicle low altitude remote sensing field boundary extraction method
摘要
Abstract
[Objective]To develop a new method for extracting field boundaries from low-altitude remote sensing ima-ges of unmanned aerial vehicle(UAV),which could provide technical support to address the challenges of extracting boundary information for small fields in hilly and mountainous areas.[Method]Utilized low-altitude remote sensing tech-nology from UAV to acquire high-resolution images of specified fields,creating a single-field image dataset.Based on the RCF model,a deep learning edge detection algorithm,an improved RCF(IRCF)model with higher accuracy and perfor-mance was constructed by reducing pooling layers and incorporating an attention mechanism module for field boundary recognition.Subsequently,the recognized boundary images treated by contour detection and quantization processing,converting continuous boundary lines into discrete boundary lines composed of multiple pixel points to obtain pixel coordi-nates.These coordinates were then transformed into plane coordinates suitable for agricultural machinery operations using the Gauss-Krüger projection.The accuracy of field boundary extraction was validated using 2 indicators:the effective field area rate of field and the average coordinate deviation.[Result]Compared to the RCF model,the IRCF model demon-strates improved algorithmic performance.Its test results showed a 1.8368% lead in the F1 score at the optimal dataset scale(ODS)and a 2.7969% lead in the F1 score at the optimal image scale(OIS),with an average precision(AP)in-crease of 3.8540% .After quantization processing of the field boundary lines,the total number of pixel points within the in-ner boundary area was calculated to be 21339,which further converted to an extracted field boundary area of 1841.32 m2,with an extraction rate of 91.02% for the IRCF model.By combining the IRCF model with Canny operator boundary quan-tization and comparing the actual measured values with the extracted values,it was found that the average deviation of plane coordinates in the X-axis direction was 0.613 m,and in the Y-axis direction was 0.744 m,both were less than 0.800 m,indicating that the model could provide relatively accurate boundary coordinate information.[Conclusion]The low-altitude remote sensing field boundary extraction method based on the IRCF model can address the challenge of traditional edge detection algorithms in identifying narrow field boundaries within field image datasets.The obtained field boundary images,after contour detection and quantization processing,enable accurate acquisition of plane coordinates for field boundaries.This method achieves highly efficient automatic extraction of boundary information for small fields in hilly and mountainous areas.关键词
田块边界/IRCF模型/边缘检测算法/无人机低空遥感影像/坐标获取Key words
field boundary/IRCF model/edge detection algorithm/low-altitude remote sensing images from un-manned aerial vehicle/coordinate acquisition分类
农业科技引用本文复制引用
何思涵,胡佳慧,何忠秀,李青涛,王霜..基于IRCF模型的无人机低空遥感影像田块边界提取方法研究[J].南方农业学报,2025,56(1):111-123,13.基金项目
国家自然科学基金项目(51905447) （51905447）
四川省现代农业装备工程技术研究中心课题(RX2300001944) National Natural Science Foundation of China(51905447) （RX2300001944）
Sichuan Modern Agricultural Equip-ment Engineering and Technology Research Center Project(RX2300001944) （RX2300001944）
