Stitching and Reconstruction of Linear-Pushbroom Panoramic Images for Planar Scenes

Abstract

Abstract. This paper proposes a method to integrate multiple linearpushbroom panoramic images. The integration can be performed in real time. The technique is feasible on planar scene such as large-scale paintings or aerial/satellite images that are considered to be planar. The image integration consists of two steps: stitching and Euclidean reconstruction. For the image stitching, a minimum of five pairs of noncollinear image corresponding points are required in general cases. In some special configurations when there is column-to-column image correspondence between two panoramas, the number of image corresponding points required can be reduced to three. As for the Euclidean reconstruction, five pairs of non-collinear image corresponding points on the image boundaries are sufficient.

Mosaicking of Unmanned Aerial Vehicle Imagery in the Absence of Camera Poses

“…In many digital earth applications, it is therefore necessary to stitch hundreds or even thousands of images together to create a larger image that can provide good overall situational awareness. Mosaicking of UAV imagery usually requires extra information, such as camera calibration parameters, position and orientation data from GPS/IMU, ground control points (GCPs) or a reference map, to achieve accurate mosaicking results [1][2][3][4][5][6][7]. When GPS/IMU data are not accurate enough for direct orientation determination, pose estimation using a 3D reconstruction method is usually employed to refine the camera A single image captured from a typical UAV covers only a limited area on the ground.…”

“…To enlarge the field of view, feature-based pairwise image registration can be applied to generate precise short-term mosaicking with almost no visual artifacts. Compared to linear-pushbroom sensor mosaicking [4], this method has the advantage that non-linear flight-paths and varying view directions can be handled effectively. However, stitching hundreds, or even thousands, of images would result in a significant drift of parameters due to accumulated errors.…”

Ghost Elimination via Multi-Component Collaboration for Unmanned Aerial Vehicle Remote Sensing Image Stitching

“…UAV image mosaic usually needs additional information, such as camera calibration parameters, and position and rotation data from the GPS/IMU, ground control points or a reference map, to achieve accurate mosaic results [17][18][19][20][21][22][23][24]. When the GPS/IMU data are not sufficiently accurate, the stitching may be affected.…”