| Summary: | 碩士 === 國立交通大學 === 土木工程系所 === 103 === Unmanned Aerial Vehicle (UAV) is an advance Remote Sensing (RS) technology in data acquisition. UAV has many advantages such as low cost, high flexibility, high spatial resolution and high overlapped images. UAV has been widely used in many fields such as mapping, 3D city modeling, emergency rescue, change detection and etc., in which the image quality plays an important role of UAV’s applications. However, the small wings-fixed UAV is suffering from the image blur due to crosswind and turbulence, degrading the production quality. Traditionally, small-frame UAV requires a large number of images for processing, and it is labor-intensive to identify the blurred images manually. The automation of blurred image detection and restoration will save processing time and improve final product qualities of UAV.
In this study, we proposed a Position and Orientation System (POS) assisted method to detect the blurred image, and discussed the influence of motion blur images for photogrammetric purposes. The major steps include: blurred image detection, restoration and verification. In blurred image detection, we modified the traditional degree-of-linear-blur (blinear) method to degree-of-motion-blur (bmotion) based on the collinear condition equation to describe the degree of blur of UAV images. Support Vector Machines (SVM) classifier was adopted for blur detection and feature extraction (e.g., image information, POS data, blinear and bmotion). In addition, we used a shaking table to simulate the linear motion, and discussed the impact factors of image blurs. Lucy–Richardson deconvolution was to conduct blurred image restoration. The kernel size and the rotation angle of Point Spread Function (PSF) (also known as blur kernel) were defined by degree-of-motion-blur.
In verification, we explored the impacts of blurs from two aspects: feature extraction and image matching. In feature extraction, we applied Speeded up robust features (SURF) and Canny Edge Detector to extract the feature points and lines, respectively. In image matching, we used SURF matching to evaluate the influence of image blurs.
The experiment was performed using SenseFlyeBee UAV system with the location on Kuangfu campus, National Chiao Tung University (NCTU). The total number of image taken was 129. In blurred image detection, we used the proposed degree-of-motion-blur as a feature to classify the blurred and sharp images, and obtained the overall accuracy of 76%. Besides, the number of features and the image matching success rate were associated with the degree of blur. Higher degree of blur hindered number of extracted features and image matching rate. The number of features and the image matching success rate increased after blur restoration. The improvement rates for feature point and line were 57% and 30%, respectively. In image matching, although the number of matching pair reduced, the correctness increased. The experimental results revealed that blurred image restoration can benefit photogrammetry in product quality and processing time
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