计算机与现代化Issue(4)：83-88,6.DOI:10.3969/j.issn.1006-2475.2025.04.013
基于图像分割的无人机影像AGP计算方法
AGP Calculation Methods in UAV Imagery Based on Image Segmentation
摘要
Abstract
Grassland degradation is a critical issue in the Three Rivers Source Region that cannot be overlooked.Employing deep learning techniques for the evaluating of grassland degradation in the Three Rivers Source Region is a pivotal step towards intelli-gent grassland assessment.However,a challenge in semantic segmentation lies in the potential inconsistency of altitudes in UAV-captured imagery,which can lead to discrepancies between computed proportions of poisonous weed cover and actual con-ditions,consequently introducing errors in grassland degradation assessment.This study proposes a method to calculate the Ac-tual Ground Proportion(AGP)for both known and unknown heights of captured grassland images.For images with known heights,we select to utilize the captured altitude for AGP calculation and then map images of varying altitudes to a common height for coverage computation.For images with unknown heights,we train a sorrel instance segmentation model to calculate AGP based on instance segmentation results,followed by coverage computation.Experimental restlts demonstrate that,in com-parison to direct coverage calculation,the use of instance segmentation reduces the error from 2.7%to 0.39%.This approach holds significant importance in enhancing the accuracy of intelligent grassland degradation assessment.关键词
深度学习/实例分割/无人机影像/草地退化Key words
deep learning/instance segmentation/UAV imagery/grassland degradation分类
信息技术与安全科学引用本文复制引用
李楷,金云鹏,李海洋,孔莎莎,杨鹏,方成梧,黄湘杰,韩耀升,李春梅..基于图像分割的无人机影像AGP计算方法[J].计算机与现代化,2025,(4):83-88,6.基金项目
国家自然科学基金资助项目(62166033) （62166033）
