摘要
针对卫星影像难以获取大坡度地质露头数据、传统分类方法无法有效利用影像信息导致地质露头剖面岩性分类精度较低等问题,本文通过无人机遥感技术获取高精度野外大坡度地质露头数据,并提出了一种多尺度混合特征网络模型。研究结果表明:无人机与贴近摄影测量技术相结合的方法在采集地质露头数据的应用中具有较强的可行性;多尺度混合特征网络模型能够有效地提取无人机多光谱影像中的光谱特征和空间特征,实现了大坡度地质露头岩性的高精度分类。以云台山地质公园某露头为例,本文模型的总体分类精度可达89.91%,Kappa系数可达0.85,相比于传统的机器学习算法SVM和MLC精度提高接近15%,相比于Inception V3和ResNet18总体分类精度提高约10%,相比于Hybrid CNN总体分类精度提高1.5%。
Aiming at the problems that satellite images are difficult to obtain geological outcrop data with large slopes,and traditional classification methods cannot effectively use image information leading to geological outcrop section lithology classification accuracy being relatively low,this research obtains high-precision field geological outcrop data with large slope based on UAV remote sensing technology and proposes a multi-scale hybrid feature network model.The results show that the combination of UAV and close photogrammetry is feasible in collecting geological outcrop data.The multi-scale hybrid feature network model can effectively extract the spectral features and spatial features from the multi-spectral images of UAV and realize the high-precision lithology classification of geological outcrops with large slopes.Taking an outcrop in Yuntaishan geopark as an example,the overall classification accuracy of the proposed model can reach 89.91%,and the Kappa coefficient can reach 0.85.The general classification accuracy is nearly 15%higher than traditional machine learning algorithms SVM and MLC,about 10%higher than Inception V3 and ResNet18,and 1.5%higher than Hybrid CNN.
作者
常乐
韩磊
陈宗强
盛辉
刘善伟
CHANG Le;HAN Lei;CHEN Zongqiang;SHENG Hui;LUI Shanwei(Qingdao Surveying&Mapping Institute,Qingdao 266033,China;College of Oceanography and Space Informatics in China University of Petroleum(East China),Qingdao 266580,China;National-local Joint Engineering Research Center of Integration and Application of Marineterrestrial Geographical Information(Qingdao),Qingdao 266033,China)
出处
《测绘通报》
CSCD
北大核心
2023年第11期42-47,共6页
Bulletin of Surveying and Mapping
关键词
无人机
地质露头
深度学习
岩性分类
UAV
geological outcrop
deep learning
lithology classification
