基于卷积神经网络的卫星遥感图像拼接

针对传统算法不适用于外观发生较大变化的图像拼接问题, 提出一种基于卷积神经网络的遥感图像拼接方法, 通过深度学习使模型实现对遥感图像的配准和拼接. 通过两次实验将该算法与传统算法进行对比. 首先, 以欧氏距离作为评价指标, 分别通过两种算法在不同图像数据集上进行统计, 对它们的图像配准能力进行评估. 其次, 在真实的遥感图像拼接应用场景下对比两种算法实现的遥感图像拼接效果. 实验结果表明, 卷积神经网络模型对外观发生较大形变的图像具有更好的配准能力, 因此对于外观产生较大变化的遥感图像, 可采用该算法代替传统算法实现图像拼接, 得到更精确的全景图像.

Satellite Remote Sensing Image Mosaic Based on Convolutional Neural Network

Aiming at the problem that traditional algorithms were not suitable for image mosaic with large changes in appearance, we  proposed a remote sensing image mosaic method based on convolutional neural network, which enabled the model to realize the registration and mosaic of remote sensing images through deep learning. The algorithm was compared with the traditional algorithms through two experiments. Firstly, the Euclidean distance was used as the evaluation indicator, and statistics of two methods were performed on different remote sensing image data sets to evaluate their image registration abilities. Secondly, effects of remote sensing image mosaic realized by two algorithms in the real remote sensing image mosaic application scene were compared. The experimental results show that the convolutional neural network model has a better registration ability for images with large external deformations. Therefore, for the remote sensing images with large changes in appearance, the proposed algorithm can be used to replace the traditional algorithm to realize image mosaic and obtain a more accurate panoramic image.