Abstract:  In order to provide a panoramic image with high precision and wide field, the main purpose of this
study is to achieve a high-resolution images stitching. For a large number of high-resolution farmland remote
sensing images taken by UAV(Unmanned Aerial Vehicle), to obtain the full panoramic farmland image,
the image mosaic algorithm is improved by combining with characteristics of UAV image. In detecting the
candidate points step preliminarily using SIFT ( Scale-Invariant Feature Transform) algorithm, we use
adaptive threshold to remove part of the candidate feature points. By combining with latitude, longitude
coordinates and the relative position relation of the overlapping area about UAV image, we remove part of
invalid feature points and make rough matching on feature point. For completing two adjacent farmland
remote sensing images stitching, we apply random sample consensus algorithm to eliminate mismatching
point, and solve the projective transformation matrix. In order to complete 128 high-resolution images
stitching, we design pyramid stitching strategy. The experimental results show that: with the improved
feature points streamline method on SIFT algorithm, the time needed for the rough feature points matching is
reduced by an average of 52% , and 25% reduction for Accurate feature points matching. In comparison
experiment based on six evaluation parameters, we found that the multi-resolution image fusion algorithm is
superior to other fusion algorithms in qualitative and quantitative analysis. The study provides an efficient
reference for a large number of high-resolution image stitching.

Key words: SIFT algorithm, image fusion, feature point matching, farmland remote sensing image stitching, unmanned aerial vehide(UAV)