Abstract
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.
Highlights
As a new remote sensing technology, UAV remote sensing was widely used in natural disaster monitoring, land resource management, land use survey, agricultural production activities, environmental pollution monitoring, urban construction, coastal zone monitoring, because of its advantages in high resolution, flexible operation, low cost, low-flying under clouds and ability to work full-time (Lei et al.,2016)
The innovation of this paper was that GPU was used to accelerate feature point extraction and Progressive Sample Consensus (PROSAC) algorithm was used to remove a large number of matching point pairs;we used GPU parallel operation to accelerate the calculation of improved SURF algorithm and PROSAC algorithm was used to improve the accuracy of remote sensing image registration based-on SURF algorithm
We used PROSAC algorithm to improve the accuracy of remote sensing image registration based-on SURF algorithm, and GPU parallel operation was used to accelerate the calculation of improved SURF algorithm
Summary
As a new remote sensing technology, UAV remote sensing was widely used in natural disaster monitoring, land resource management, land use survey, agricultural production activities, environmental pollution monitoring, urban construction, coastal zone monitoring, because of its advantages in high resolution, flexible operation, low cost, low-flying under clouds and ability to work full-time (Lei et al.,2016). UAV remote sensing image has the problems of small coverage area, large number, irregular overlap, which cannot meet the actual needs, such as natural disaster relief (Lei et al.,2017). It is difficult to quickly and accurately stitch a regional panorama for lots of UAV remote sensing images. How to quickly and efficiently splice a lot of UAV remote sensing images and obtain a regional full coverage, wide field of vision and high precision image is important. According to the different methods of using image information, the image matching algorithms was divided into gray-based and feature-based matching technology (Zhu et al.,2017). Image matching algorithm based on gray level had been studied earlier in theory but less in practice (Kuglin,Hines,1975). It was difficult to obtain satisfactory results using gray-based registration methods
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