원문정보
초록
영어
UAV remote sensing, as a new method of remote sensing, has the characteristics of higher spatial resolution, fine timeliness and high flexibility. It is widely used in the field of natural disaster monitoring, urban planning, resource investigation, and has become one of the indispensable method of remote sensing data acquisition. However, because the UAV remote sensing platform is limited by the flight height and focal length of camera, the acquired image size is smaller, single image can’t cover the entire target area. Therefore, image mosaic has become a key technology to solve the problem. Image matching and image fusion are the key techniques of image mosaic. Due to the good robustness of image scaling, translation and rotation, this paper uses the SIFT algorithm to realize image matching of UAV. Since the feature extraction may produce false matches, RANSAC algorithm is applied to the feature point purification points. According to the seam-line in jointing overlap region, weighted fusion algorithm is applied to realize the image seamless splicing.
목차
1. Introduction
2. UAV Remote Sensing Image Mosaic Process
2.1. Image Preprocessing
2.2. Image Registration
2.3. Image Fusion
3. SIFT Algorithm Principle
3.1. Construction of Scale Space
3.2. Keypoint detection
3.3. Determination of the Keypoint
3.4. Generation of Keypoint Descriptor
4. The Experimental Environment and Data
5. The Experimental Results and Analysis
5.1. Image Read
5.2. Gaussian Pyramid Build
5.3. Difference of Gaussian Pyramid Build
5.4. Feature point extraction
5.5. Feature point matching
5.6. Feature Point Purification
5.7. Image fusion
6. Conclusions
References