Multi-Scale Fused SAR Image Registration Based on Deep Forest
Multi-Scale Fused SAR Image Registration Based on Deep Forest
复制标题
基于深度森林的多尺度融合SAR图像配准
DOI：
10.3390/rs13112227
复制
发表时间：
2021-06-01
期刊：
影响因子：
5
通讯作者：
Xiong, Lin
中科院分区：
文献类型：
--
作者：
Mao, Shasha;Yang, Jinyuan;Xiong, Lin
SAR图像配准是SAR图像处理中的一个关键问题，高精度的配准结果有助于提高SAR图像变化检测等其它问题的质量。目前，对于大多数基于DL的SAR图像配准方法，SAR图像配准问题被视为具有匹配和非匹配类别的二元分类问题来构建训练模型，其中通常设置固定尺度来捕获与关键点对应的成对图像块以生成训练集，而已知不同尺度的图像块包含不同的信息，这影响了配准的性能。此外，关键点的数量不足以生成大量的类平衡训练样本。为此，本文提出了一种新的SAR图像配准方法，同时利用多尺度信息建立匹配模型。具体而言，考虑到训练样本的数量很少，使用深度森林来训练多个匹配模型。此外，提出了一种多尺度融合策略，以整合多个预测，并获得参考图像和传感器图像之间的最佳配对匹配点。最后，在4个数据集上的实验结果表明，该方法的配准效果优于现有方法，不同尺度下的配准结果分析也表明，多尺度融合比单一固定尺度的配准更有效、更鲁棒。
SAR image registration is a crucial problem in SAR image processing since the registration results with high precision are conducive to improving the quality of other problems, such as change detection of SAR images. Recently, for most DL-based SAR image registration methods, the problem of SAR image registration has been regarded as a binary classification problem with matching and non-matching categories to construct the training model, where a fixed scale is generally set to capture pair image blocks corresponding to key points to generate the training set, whereas it is known that image blocks with different scales contain different information, which affects the performance of registration. Moreover, the number of key points is not enough to generate a mass of class-balance training samples. Hence, we proposed a new method of SAR image registration that meanwhile utilizes the information of multiple scales to construct the matching models. Specifically, considering that the number of training samples is small, deep forest was employed to train multiple matching models. Moreover, a multi-scale fusion strategy is proposed to integrate the multiple predictions and obtain the best pair matching points between the reference image and the sensed image. Finally, experimental results on four datasets illustrate that the proposed method is better than the compared state-of-the-art methods, and the analyses for different scales also indicate that the fusion of multiple scales is more effective and more robust for SAR image registration than one single fixed scale.