摘要
野生鹿科(Cervidae)动物作为生态系统的组成部分,在维持生态平衡方面扮演着至关重要的角色。无人机影像技术在野生动物监测中的应用已日趋成熟,但受自然光照条件和野外环境复杂多变的影响,单一光谱成像技术很难得到高质量的野生鹿科动物图像。因此提出一种基于DenseFuse网络的图像融合算法,通过无人机搭载的多光谱成像设备,将红外图像与可见光图像融合,同时保留红外图像的轮廓信息和可见光图像的外貌信息,提高了监测图像质量。基于野生鹿科动物影像数据集,采用多种图像融合策略进行实验,对红外图像与可见光图像的融合效果展开细致对比。结果显示,通过使用l1-norm融合策略所获得的综合评价指标最优,经该策略融合后得到的图像平均信息熵达到了6.965。这一结果表明,本研究所提出的无人机多光源图像融合算法能够为野生动物监测工作提供可靠的技术支撑。
As an integral part of the ecosystem,wild Cervidae animals play a crucial role in maintaining ecological balance.The application of unmanned aerial vehicle(UAV)imaging technology in wildlife monitoring has become increasingly mature.However,due to the influence of natural lighting conditions and the complex and changeable wild environment,it is difficult to obtain high-quality cervid images using single-spectrum imaging technology.Therefore,this paper proposes an image fusion algorithm based on the DenseFuse network.By utilizing the multispectral imaging equipment carried by un⁃manned aerial vehicles(UAVs),the algorithm fuses infrared images with visible light images while preserving the contour information of the infrared images and the appearance information of the visible light images,thereby improving the quality of monitoring images.Based on the wild cervid image dataset,this paper employs multiple image fusion strategies for ex⁃periments and conducts a detailed comparison of the fusion effects between infrared and visible light images.The experi⁃mental results show that the comprehensive evaluation index obtained by using the l1-norm fusion strategy is the best,and the average information entropy of the fused images reaches 6.965.This result indicates that the proposed UAV multisource image fusion algorithm can provide reliable technical support for wildlife monitoring.
作者
李汶佼
包衡
杜化林
李洋
张卫华
杨琨
马光凯
姜广顺
LI Wenjiao;BAO Heng;DU Hualin;LI Yang;ZHANG Weihua;YANG Kun;MA Guangkai;JIANG Guangshun(College of Computer and Control Engineering,Northeast Forestry University,Harbin 150040,China;Feline Research Center of National Forestry and Grassland Administration,College of Wildlife and Protected Area,Northeast Forestry University,Harbin 150040,China;Inner Mongolia Forestry Industry Group,Yakeshi 022150,China;Inner Mongolia Hanma National Nature Reserve,Genhe 022359,China)
出处
《野生动物学报》
北大核心
2025年第3期514-522,共9页
CHINESE JOURNAL OF WILDLIFE
基金
国家重点研发计划项目(2023YFF1305000)
中央高校基本科研业务费专项基金项目(2572022DS04)。
