Image registration and selection for unmanned aerial vehicle image stitching

Abstract. Remote sensing system fitted on UAV (Unmanned Aerial Vehicle) can obtain clear images and high-resolution aerial photographs. It has advantages of flexibility, convenience and ability to work full-time. However, there are some problems of UAV image such as small coverage area, large number, irregular overlap, etc. How to obtain a large regional map quickly becomes a major obstacle to UAV remote sensing application. In this paper, a new method of fast registration of UAV remote sensing images was proposed to meet the needs of practical application. This paper used Progressive Sample Consensus (PROSAC) algorithm to improve the matching accuracy by removed a large number of mismatching point pairs of remote sensing image registration based-on SURF (Speed Up Robust Feature) algorithm, and GPU (Graphic Processing Unit) was also used to accelerate the speed of improved SURF algorithm. Finally, geometric verification was used to achieve mosaic accuracy in survey area. The number of feature points obtained by using improved SURF based-on PROSAC algorithm was only 9.5% than that of SURF algorithm. Moreover, the accuracy rate of improved method was about 99.7%, while the accuracy rate of improved SURF algorithm was increased by 8% than SURF algorithm. Moreover, the improved running time of SURFGPU algorithm for UAV remote sensing image registration was a speed of around 16 times than SURF algorithm, and the image matching time had reached millisecond level. Thus, improved SURF algorithm had better matching accuracy and executing speed to meet the requirements of real-time and robustness in UAV remote sensing image registration.

Download Full-text

An unmanned aerial vehicle optimal selection methodology for object tracking

Advances in Mechanical Engineering ◽

10.1177/1687814018814085 ◽

2018 ◽

Vol 10 (12) ◽

pp. 168781401881408

Author(s):

Antoni Kopyt ◽

Janusz Narkiewicz ◽

Paweł Radziszewski

Keyword(s):

Unmanned Aerial Vehicle ◽

Individual Performance ◽

Trajectory Prediction ◽

Optimal Method ◽

Flying Qualities ◽

Ground Target ◽

Aerial Vehicle ◽

Vehicle Fleet ◽

Selection For ◽

Selection Methodology

In this article, an optimal method of unmanned aerial vehicle selection for following a moving ground target is presented. The most suitable unmanned aerial vehicle from an aircraft operating fleet is selected for tracking task. The aircraft choice is done taking regarding the unmanned aerial vehicle distribution in the mission zone, individual performance of fleet platforms, and maneuverability of a ground platform. The unmanned aerial vehicle fleet members’ flying qualities are various, so an optimization process is used to assign an aircraft to follow the ground target. A target trajectory prediction is embedded in tracking algorithm. The simulation results demonstrated the effectiveness of algorithms developed, and in-flight demonstration proved the possibility of realization of computed trajectories by the unmanned aerial vehicle.

Download Full-text

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

Remote Sensing ◽

10.3390/rs13071388 ◽

2021 ◽

Vol 13 (7) ◽

pp. 1388

Author(s):

Wanli Xue ◽

Zhe Zhang ◽

Shengyong Chen

Keyword(s):

Remote Sensing ◽

Unmanned Aerial Vehicle ◽

Moving Objects ◽

Information Source ◽

Remote Sensing Image ◽

Selection Strategy ◽

Image Stitching ◽

Data Set ◽

Image Information ◽

Aerial Vehicle

Ghosts are a common phenomenon widely present in unmanned aerial vehicle (UAV) remote sensing image stitching that seriously affect the naturalness of stitching results. In order to effectively remove ghosts and produce visually natural stitching results, we propose a novel image stitching method that can identify and eliminate ghosts through multi-component collaboration without object distortion, segmentation or repetition. Specifically, our main contributions are as follows: first, we propose a ghost identification component to locate a potential ghost in the stitching area; and detect significantly moving objects in the two stitched images. In particular, due to the characteristics of UAV shooting, the objects in UAV remote sensing images are small and the image quality is poor. We propose a mesh-based image difference comparison method to identify ghosts; and use an object tracking algorithm to accurately correspond to each ghost pair. Second, we design an image information source selection strategy to generate the ghost replacement region, which can replace the located ghost and avoid object distortion, segmentation and repetition. Third, we find that the process of ghost elimination can produce natural mosaic images by eliminating the ghost caused by initial blending with selected image information source. We validate the proposed method on VIVID data set and compare our method with Homo, ELA, SPW and APAP using the peak signal to noise ratio (PSNR) evaluation indicator.

Download Full-text