面向消防救援指挥的无人机航拍快速成图方法OA

This study proposes a semantic information-based aerial image stitching methodology.In the first stage,a coarse registration method using projection transformation based on 3D vector rotation is adopted.Through geographic coordinate conversion,view frustum modeling,plane intersection calculation,and projection matrix estimation,efficient coarse registration of aerial images onto a unified elevation plane is achieved.In the second stage,a semantic segmentation and LightGlue feature based optimization method is utilized.Each row of images after coarse registration is treated as an independent optimization unit:road topological structures are obtained via semantic segmentation,and high-precision registration is realized by combining a lightweight feature matching network with geometric constraint optimization.Experimental results show that the proposed two-stage optimization model effectively overcomes the dependence of camera intrinsics.Finally,the accurate disaster-stricken locations are visualized on the geographic system interface,enabling comprehensive and systematic recording of disaster characteristics.