无人机影像重叠度较高，幅数过多，影像拼接需要耗费大量时间，在不影响数据质量的情况下，如何降低航片的冗余是本论文研究的主要问题。 论文以甘肃某县土地确权项目为依托，以实测无人机影像为基础数据，对减小无人机影像重叠度，减少数据冗余进行了分析研究，具体研究内容及结果如下： 1.首先对研究区域无人机数据做了影像重叠度统计，结合无人机最佳重叠度和规范要求，发现无人机影像存在大量冗余。针对无人机影像高重叠度的特点，采用不同间隔的抽稀方式对影像稀释。实验结果表明影像抽稀间隔越大，拼接影像所用时间越少，平面位置中误差越大。间隔4张影像抽稀达到影像重叠度规范临界值，拼接的时间比未抽稀影像拼接所用时间减少了一半多。 2.通过构建一个包含138幅航空遥感影像的样本库，利用知识库的方法，在10分制的基础上，提出了利用加权平均标准差、平均梯度和信息熵三种评价指标得到影像质量的综合评价值，建立一种基于综合评价值的抽稀方式。其影像拼接比全部影像拼接短了近一半时间，融合影像平面位置中误差得到的良好控制，曝光过度影像引起的“白斑”现象明显变少，影像辨识度明显提高。 3.影像裁剪也降低影像重叠度，加快无人机影像影像匹配速度。同时，无人机影像影像边缘畸变较大，裁剪后的影像边缘基本被剪掉，可以大大提高影像的配准精度。针对影像裁剪的这一特点，提出影像裁剪和抽稀相结合的方式对影像进行处理，进一步缩短影像拼接用时，改善了影像边缘畸变对影像拼接成图的影响。

Usually the overlap degree of UAV images of a region is too high, the quantity of the UAV images of a region is too large, so image mosaic takes a lot of time. It’s seriously affect the work efficiency. Therefore, in order to meet the needs of production, improve the efficiency of image mosaic is very important. Based on the confirmation of land right in Gansu province and the data of the unmanned aerial vehicle (UAV) images, the reduction methods of overlap degree and quantity of UAV images are analyzed and researched. The specific contents and results are as follows: 1.First of all, the overlap degree statistics of UAV images of the study area has be made, combined with UAV optimal overlap degree and specification requirements, we found that UAV images are full of redundancy. For the characteristics of UAV images with high overlap, the images are diluted with different intervals. The experimental results show that the larger image shrinkage interval, the less time to mosaic the image, the more error in the plane position. 4-image interval make image overlap degree reach the specification threshold, mosaic time of these images is less than half of the mosaic time of all non-thinning images. 2.Through the construction of a sample library containing 138 UAV images, the author uses the knowledge base method to put forward the comprehensive evaluation value of image by using the weighted average standard deviation, the average gradient and the information entropy on the basis of the 10-point system. One image-thinning method is established based on the comprehensive evaluation value of image. The time of image mosaic by this method is less than the time of total image mosaic nearly the half time, the error of image plane position is in the good control, the "white spot" phenomenon caused by excessive exposure image is significantly less than before, and image recognition is significantly improved. 3.Image cutting also can reduce the image overlap and speed up the UAV image matching. At the same time, image distortion of the UAV image is larger at the edge of the image, after image cutting the edge of the image is basically cut off, which can greatly improve the registration accuracy of the image. Due to the benefits of image cutting, one method combined image cutting with image thinning is put forward to deal with the images, the time of image mosaic is less, Which reduces the influence of image edge distortion on image mosaic.