Abstract: How to achieve fast and accurate image stitching to obtain large-area, high-resolution aerial remote sensing images is a key problem in the field of image mosaic research. Aiming at the problems of poor matching accuracy and slow registration speed in the current UAV (unmanned aerial vehicle) remote sensing image registration algorithm, based on the point feature detection method and the matrix dimensionality reduction processing method, an improved algorithm SNS (scale-invariant feature transform and singular value decomposition) algorithm, which is suitable for the registration of agricultural aviation remote sensing images was proposed in this paper. SNS algorithm detects the extreme value point of scale, the characteristics of the scale for the feature points, use hessian matrix to eliminate the false feature points to precise positioning feature points. Therefore, SNS algorithm can simultaneously detect the coordinates of feature points and feature dimension. The advantage of SNS algorithm lies in the singular value decomposition and reconstruction of the image, which will reduce the feature points of the reconstructed image, especially the feature points that are not important or obvious, so as to reduce the unnecessary calculation amount of finding feature matching pairs and improve the registration speed and accuracy. SNS algorithm uses SVD method for matrix decomposition, realizing data dimensionality re-reconstruction, compressing data volume, and the overall registration accuracy is improved as well. The experimental image consists of the reference image of infrared remote sensing image collected by UAV, the original image, and five images which is registered after affine transformation from the original image, the reference image resolution is 640 × 512, the original image resolution is 640 × 512, scale-up image resolution is 950 × 760, scale-down image resolution is 400 × 320, the resolution of rotated original image by 30° is 811 × 764, rotated by 30° and scale-up image resolution is 1000 × 942, rotated by 30° and scale-down image resolution is 500 × 471. SIFT, SNS, SURF (speed-up robust features) and Harris algorithms are selected to run 100 times for comparison and analysis. The results show that harris algorithm is suitable for image registration with little scale change and small rotation angle, but cannot complete registration in the case of small scale change or overlap area, so it is limited in the registration of agricultural aerial remote sensing images. SURF algorithm combines the characteristics of integral image and window filter, and has the advantage of fast registration speed, however, because of using approximate Gaussian filter and approximate gradient method to improve the registration speed at the expense of registration accuracy, it is not suitable in agricultural aviation remote sensing image registration with attention to registration accuracy. SNS and SIFT algorithm can be used for image registration in various cases. And the registration speed of SNS algorithm is 5.01% faster than SIFT algorithm, and the RMSE ( root mean squared error )of SNS algorithm is reduced by 10.48%. In order to further compare the processing efficiency of SIFT algorithm and SNS algorithm, multiple remote sensing images test is carried out. The test image data set consists of 160 drone 50 m low-altitude remote sensing images, each with a resolution of 437 × 800. The collection area is about 3.13 hm2. Each algorithm runs 50 times and record the registration time. The experimental results show that the total registration time of SNS algorithm is 10.34% less than that of SIFT algorithm, which shows that the registration speed of SNS algorithm in this experiment is better than SIFT algorithm. Obviously, SNS algorithm has the advantages of fast speed and high precision in the registration of agricultural aerial remote sensing images, which can provide useful guidance for intelligent agriculture to obtain large-area agricultural regional images quickly and accurately for field management, crop management, pest management, yield prediction and other applications.