Abstract
To improve the efficiency and effectiveness of mosaicking unmanned aerial vehicle (UAV) images, we propose in this paper a rapid mosaicking method based on scale-invariant feature transform (SIFT) for mosaicking UAV images used for crop growth monitoring. The proposed method dynamically sets the appropriate contrast threshold in the difference of Gaussian (DOG) scale-space according to the contrast characteristics of UAV images used for crop growth monitoring. Therefore, this method adjusts and optimizes the number of matched feature point pairs in UAV images and increases the mosaicking efficiency. Meanwhile, based on the relative location relationship of UAV images used for crop growth monitoring, the random sample consensus (RANSAC) algorithm is integrated to eliminate the influence of mismatched point pairs in UAV images on mosaicking and to keep the accuracy and quality of mosaicking. Mosaicking experiments were conducted by setting three types of UAV images in crop growth monitoring: visible, near-infrared, and thermal infrared. The results indicate that compared to the standard SIFT algorithm and frequently used commercial mosaicking software, the method proposed here significantly improves the applicability, efficiency, and accuracy of mosaicking UAV images in crop growth monitoring. In comparison with image mosaicking based on the standard SIFT algorithm, the time efficiency of the proposed method is higher by 30%, and its structural similarity index of mosaicking accuracy is about 0.9. Meanwhile, the approach successfully mosaics low-resolution UAV images used for crop growth monitoring and improves the applicability of the SIFT algorithm, providing a technical reference for UAV application used for crop growth and phenotypic monitoring.
Highlights
Remote sensing from an unmanned aerial vehicle (UAV) is an emerging monitoring technique
Due to the characteristics of crop UAV images, the method optimizes the dynamic setting of the contrast threshold Dc in the difference of Gaussian (DOG) scale-space in scale-invariant feature transform (SIFT) to improve the efficiency of the algorithm; we delete the mismatched point pairs to improve the mosaicking accuracy based on the relative position relationships of the UAV images
In comparison with the standard SIFT (SSIFT)-based image mosaicking method, the proposed method could increase the time efficiency of mosaicking by approximately 30% while this increase is approximately 20% compared with PS
Summary
Remote sensing from an unmanned aerial vehicle (UAV) is an emerging monitoring technique. Farmland crop growth is mainly monitored by rotary-wing and fixed-wing UAVs with UAV flight plans including flight altitude and flight speed [8,9]. These UAVs carry different sensors such as RGB, NIR, thermal infrared, and hyperspectral sensors, and images acquired by different sensors usually have different resolutions [2,3,4]. In the manual mode for image acquisition, the operator of the UAV triggers the camera and takes photos manually during the flight. In the fixed-point mode, the UAV flies following a predefined path and stops at its location for image acquisition. The images are taken during the flight without stopping flying
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