Image registration is one of the most important processes for the generation of remote sensing image mosaics. This paper focuses on the special problems related to remote sensing data registration, and multi-temporal data mosaic applications in the domain of forestry. It proposes an image registration method based on hierarchical convolutional features, and applies it to improve the efficiency of large scale forestry image mosaic generation. This method uses a deep learning architecture to adaptively obtain image features from deep convolutional neural networks. The features derived from different images at different depth are sent to a correlation filter to compute the similarity between them; then the locations of the feature points are computed precisely. Based on this method, we study forestry image registration and the mosaic framework. We apply our approach to remote sensing images under different weather and seasonal conditions, and compare the results with those generated using the traditional SIFT image mosaic method. The experimental result shows that our method can detect and match the image feature points with significant spectral difference, and effectively extract feature points to generate accurate image registration and mosaic results. This demonstrates the effectiveness and robustness of the proposed approach.

中文翻译：

---

利用卷积神经网络进行多时相数据配准的镶嵌方法在林业遥感应用中的应用

影像配准是生成遥感影像马赛克最重要的过程之一。本文重点研究遥感数据配准相关的特殊问题，以及林业领域的多时相数据镶嵌应用。提出了一种基于层次卷积特征的图像配准方法，并将其应用于提高大规模林业图像马赛克生成的效率。该方法使用深度学习架构从深度卷积神经网络自适应地获取图像特征。将不同深度的不同图像的特征发送到相关滤波器，计算它们之间的相似度；然后精确计算特征点的位置。基于这种方法，我们研究了林业图像配准和马赛克框架。我们将我们的方法应用于不同天气和季节性条件下的遥感图像，并将结果与​​使用传统 SIFT 图像镶嵌方法生成的结果进行比较。实验结果表明，我们的方法可以对光谱差异显着的图像特征点进行检测和匹配，有效提取特征点，生成准确的图像配准和拼接结果。这证明了所提出方法的有效性和稳健性。并有效提取特征点以生成准确的图像配准和拼接结果。这证明了所提出方法的有效性和稳健性。并有效提取特征点以生成准确的图像配准和拼接结果。这证明了所提出方法的有效性和稳健性。