Remote sensing image processing has been widely used in environmental monitoring, terrain survey, military investigation, disaster early warning, and other fields. The remote sensing images matching is a key step, and the accuracy and real-time of the alignment have a great impact on these applications. Due to the high resolution and complexity of remote sensing images, scale-invariant feature transformation (SIFT) algorithm has the problems of high computational complexity and poor matching effect. Adaptive threshold adjustment algorithm has proposed in the remote sensing image matching, but minor changes in the contrast threshold can bring about drastic changes in the image matching quality, which will affect applications such as monitoring, measurement, and early warning. To improve the matching quality of a SIFT detector, an adaptive contrast threshold SIFT method based on image complexity calculation (CACT-SIFT) is proposed. The CACT-SIFT finds two contrast thresholds for the target image and the reference image, respectively, and achieves the target by minimizing the proposed criteria Cik. Experiments show that the method can be applied to the matching of remote sensing images. The information of reference images and target images can be detected at the same time, and the key points can be extracted in a robust way, with better accuracy and real-time accuracy of the alignment.

中文翻译：

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遥感影像匹配对比度阈值自适应调整算法

遥感图像处理已广泛应用于环境监测、地形测量、军事调查、灾害预警等领域。遥感影像匹配是关键步骤，对中的准确性和实时性对这些应用影响很大。由于遥感图像的高分辨率和复杂性，尺度不变特征变换(SIFT)算法存在计算复杂度高、匹配效果差的问题。在遥感图像匹配中提出了自适应阈值调整算法，但对比度阈值的微小变化会导致图像匹配质量发生剧烈变化，从而影响监测、测量、预警等应用。为了提高 SIFT 检测器的匹配质量，提出了一种基于图像复杂度计算的自适应对比度阈值SIFT方法(CACT-SIFT)。CACT-SIFT 分别为目标图像和参考图像找到两个对比度阈值，并通过最小化建议的标准 Cik 来实现目标。实验表明，该方法可以应用于遥感影像的匹配。可以同时检测参考图像和目标图像信息，鲁棒性地提取关键点，具有更好的对准精度和实时精度。实验表明，该方法可以应用于遥感影像的匹配。可以同时检测参考图像和目标图像信息，鲁棒性地提取关键点，具有更好的对准精度和实时精度。实验表明，该方法可以应用于遥感影像的匹配。可以同时检测参考图像和目标图像信息，鲁棒性地提取关键点，具有更好的对准精度和实时精度。