The Review of Prospect of Remote Sensing Image Processing

Zhu, Wenzhong; Jiang, Hualong; Zhou, Shuwen; Addison, Mike

doi:10.2174/2213275909666160616115416

Cited by 1 publication

(1 citation statement)

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“…Image matching technology integrated photogrammetry, digital image processing, computer vision and pattern recognition. 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).…”

Section: Introductionmentioning

confidence: 99%

A New Method of Fast Registration of Unmanned Aerial Vehicle Remote Sensing Images Based-on an Improved Surf Algorithm

Lei

Cheng

et al. 2020

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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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.

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Section: Introductionmentioning

confidence: 99%