The purpose of image registration is to find the symmetry between the reference image and the image to be registered. In order to improve the registration effect of unmanned aerial vehicle (UAV) remote sensing imagery with a special texture background, this paper proposes an improved scale-invariant feature transform (SIFT) algorithm by combining image color and exposure information based on adaptive quantization strategy (AQCE-SIFT). By using the color and exposure information of the image, this method can enhance the contrast between the textures of the image with a special texture background, which allows easier feature extraction. The algorithm descriptor was constructed through an adaptive quantization strategy, so that remote sensing images with large geometric distortion or affine changes have a higher correct matching rate during registration. The experimental results showed that the AQCE-SIFT algorithm proposed in this paper was more reasonable in the distribution of the extracted feature points compared with the traditional SIFT algorithm. In the case of 0 degree, 30 degree, and 60 degree image geometric distortion, when the remote sensing image had a texture scarcity region, the number of matching points increased by 21.3%, 45.5%, and 28.6%, respectively and the correct matching rate increased by 0%, 6.0%, and 52.4%, respectively. When the remote sensing image had a large number of similar repetitive regions of texture, the number of matching points increased by 30.4%, 30.9%, and −11.1%, respectively and the correct matching rate increased by 1.2%, 0.8%, and 20.8% respectively. When processing remote sensing images with special texture backgrounds, the AQCE-SIFT algorithm also has more advantages than the existing common algorithms such as color SIFT (CSIFT), gradient location and orientation histogram (GLOH), and speeded-up robust features (SURF) in searching for the symmetry of features between images.

中文翻译：

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具有特殊纹理背景的遥感图像匹配研究

图像配准的目的是找到参考图像和待配准图像之间的对称性。为了提高具有特殊纹理背景的无人机（UAV）遥感影像的配准效果，本文提出了一种基于自适应量化策略的结合图像颜色和曝光信息的改进的尺度不变特征变换（SIFT）算法。 AQCE-SIFT）。通过利用图像的颜色和曝光信息，该方法可以增强具有特殊纹理背景的图像纹理之间的对比度，从而更容易地提取特征。算法描述符是通过自适应量化策略构建的，使得几何畸变或仿射变化较大的遥感图像在配准时具有较高的正确匹配率。实验结果表明，与传统的SIFT算法相比，本文提出的AQCE-SIFT算法在提取特征点的分布上更加合理。在0度、30度和60度图像几何畸变情况下，当遥感图像存在纹理稀缺区域时，匹配点数分别增加21.3%、45.5%和28.6%，正确匹配率分别增加了 0%、6.0% 和 52.4%。当遥感图像有大量相似的纹理重复区域时，匹配点数分别增加了30.4%、30.9%和-11.1%，正确匹配率增加了1.2%，分别为 0.8% 和 20.8%。在处理具有特殊纹理背景的遥感图像时，AQCE-SIFT算法也比现有的常用算法如颜色SIFT（CSIFT）、梯度位置和方向直方图（GLOH）、加速鲁棒特征（SURF）等具有更多优势寻找图像之间特征的对称性。