摘要
本文提出了一种基于光流估计的高光谱卫星遥感数据的固体废弃物识别方法。首先,从序列数据的角度看待高光谱数据,引入DeepFlow光流估计技术提取光谱维度的亮度变化信息,作为更具判别性的光谱运动特征;然后,将提取的光谱运动特征与原始光谱特征相结合后输入至常用的支持向量机进行固废识别;最后,进一步提出固废识别后处理方法改善识别效果,并利用“珠海一号”高光谱卫星遥感数据,以河南省济源示范区为研究区展开试验。试验结果表明,本文方法能够对露天堆放的工业固体废弃物进行大范围的快速精准识别,初步锁定济源示范区内存在固废遗留和违规堆放行为的11个地域风险点,且识别精度优于传统的光谱特征提取和分类方法,为后期人工现地勘察固废和“清废”行动显著节省了时间和工作量。
A solid waste identification method based on optical flow estimation for hyperspectral satellite remote sensing data is proposed in this paper.Firstly,this paper rethinks hyperspectral data from the perspective of sequence data,and proposes to introduce DeepFlow optical flow estimation technology to extract brightness change information of spectral dimension as a more discriminative spectral motion feature.Then,the extracted spectral motion features are combined with the original spectral features and input to the commonly used support vector machine for solid waste recognition to improve the recognition accuracy.Finally,a specific method of post-processing for solid waste identification is proposed to improve the identification effect.In this paper,the remote sensing data of“Zhuhai-1”hyperspectral satellite is used,and the experiment is carried out by taking Jiyuan demonstration area in Henan province as an example.The experimental results show that the proposed method can quickly and accurately identify the industrial solid wastes stacked in the open air in a wide range,and preliminarily lock the 11 regional risk points in Jiyuan demonstration area where there are solid wastes left and illegal stacking behaviors,and the accuracy is better than the traditional spectral feature extraction and classification methods.Thus,the time and workload are significantly saved for the later manual on-site investigation of solid waste and“waste clearance”action.
作者
张鹏强
孙一帆
常勍豪
刘冰
余岸竹
ZHANG Pengqiang;SUN Yifan;CHANG Qinghao;LIU Bing;YU Anzhu(Information Engineering University,Zhengzhou 450001,China)
出处
《测绘通报》
CSCD
北大核心
2024年第1期44-50,共7页
Bulletin of Surveying and Mapping
基金
河南省自然科学基金(222300420387)。
关键词
高光谱遥感
固废识别
光流估计
光谱运动特征
珠海一号
hyperspectral remote sensing
solid waste identification
optical flow estimation
spectral motion feature
Zhuhai-1
作者简介
张鹏强(1978-),男,博士,副教授,主要研究方向为高光谱数据处理、机器学习。E-mail:zpq1978@163.com;通信作者:孙一帆。E-mail:sincere_sunyf@163.com。
