摘要
针对作物遥感影像因对比度低所导致的使用尺度不变特征变换算法(scale-invariant feature transform,SIFT)提取特征点数目少,拼接效果不理想的情况,提出了一种基于图像锐化的自适应修改采样步长的非极小值抑制拼接算法,该算法在图像预处理中引入锐化滤波器对平滑后的图像进行卷积,增强图像细节,增加特征点提取数目,同时通过基于尺度的自适应修改采样步长,使图像特征点分布更加均匀,根据低对比度作物遥感影像的成像特性,采用非极小值抑制,提高图像匹配效率。在查找匹配点的过程中,引入最优节点优先算法(best-bin-first,BBF)查找最近邻与次近邻,采用随机抽样一致算法(random sample consensus,RANSAC)优选特征点。通过试验验证,该文改进后的算法相比于标准SIFT算法,在处理低空作物遥感影像时,特征点提取数目平均增加77.5%,特征点匹配对数平均增加15对,对于标准SIFT算法无法匹配的低对比度作物遥感影像,提取到了8对以上的匹配点对,满足了拼接条件。该改进算法相对于标准SIFT算法更适于低对比度遥感影像的拼接。
The technology of unmanned aerial vehicle （UAV） remote sensing takes the UAV as a remote sensing platform and the digital equipment as the mission payload. UAV is characterized by high mobility, low cost and automated acquisition of the remote sensing data. These characteristics of UAV can be used to acquire high resolution images, and therefore, they are widely used in precision agriculture, pest forecasting, crop yield prediction, and so on. However, due to the restrictions of acquisition equipment and flight height, the task area may not be fully covered in acquired images. So it is necessary to mosaic the gathered image. In the process of using the scale-invariant feature transform （SIFT） algorithm for the mosaic of crop remote sensing images, the feature points were few and the effect of mosaic was poor because of the low contrast of the crop remote sensing images. To solve this problem, the contrast experiment was conducted for analyzing the steps of SIFT algorithm using the low altitude crop remote sensing images of past years. It was noted that the images were blurred because of Gaussian transform in the stage of image preprocessing and Gaussian pyramid constructing, which led to the lack of the feature points. Aiming at this problem, we proposed to use a sharpening filter to highlight the details of the image and increase the number of feature points after the Gauss transform. In the stage of searching feature points, after analyzing the feature points on the same scale, we found the feature points concentrated in the regions where the color changed obviously; while in the different scales, the overall distribution of the feature points was inversely proportional to the scale. To prevent the reduction of the feature points caused by the same sampling step in the regions of high scale, in this paper, a search method of feature points based on adaptive sampling step was used to achieve uniform distribution of the feature points, because the uniform distribution of feature points was more conducive to image mosaic. A larger sampling step was used in the low-scale region and a smaller sampling step was used in the high-scale region. In the phase of feature matching, the feature points of crop remote sensing images were mostly concentrated at the minimum value, and a few maximum points often did not affect the image matching effect, but it would increase the amount of calculation. Therefore, this paper adopted non-minimum suppression to improve the efficiency of the algorithm. In order to verify the validity of the proposed algorithm, we carried out the experiment based on UAV remote sensing platform from July to August in 2016. Through the contrast experiment using SIFT algorithm and proposed algorithm, we could see that, as for low altitude crop remote sensing images, the number of feature points was averagely increased by 77.5% after optimization, the distribution of feature points was also more uniform, the number of matching points averagely increased by 15 pairs and the matching rate averagely increased by 9.46%; for the crop images that could not be stitched by SIFT algorithm, the proposed algorithm could extract more than 8 pairs of matching points, and this satisfied the condition of image mosaic. Experiments have showed that the custom sharpening filter can extract more feature points, and after introducing the adaptive sampling step, the distribution of the feature points is more uniform, and the optimized algorithm keeps the robustness. Therefore, this algorithm is more suitable for the crop remote sensing images mosaic than SIFT algorithm.
出处
《农业工程学报》
EI
CAS
CSCD
北大核心
2017年第10期123-129,共7页
Transactions of the Chinese Society of Agricultural Engineering
基金
国家重点研发计划专项(2016YFD0200701)
国家重点研发计划专项(2016YFD020060305)
863计划项目(2013AA102303)
关键词
作物
遥感
无人机
非极小抑制
图像拼接
特征检测
鲁棒性
crops
remote sensing
unmanned aerial vehicle
non-minimum suppression
image mosaic
sampling step
robustness
