Journal of Geo-information Science >
A Coarse-to-Fine Optical and SAR Remote Sensing Image Registration Algorithm
Received date: 2019-12-02
Request revised date: 2020-01-24
Online published: 2021-01-25
Supported by
National Natural Science Foundation of China(41701428)
Copyright
Due to the fact that optical remote sensing image and SAR image have obvious nonlinear intensity differences, and that SAR image has speckle noise, it is difficult to register them. Feature-based image registration and region-based image registration are the two most common methods of optical and SAR image registration. One advantage of feature-based image registration is that it can solve the problem of rotation, scale, and translation differences between images. Another advantage is the small amount of calculation. However, this method usually has the disadvantages of low registration accuracy and instability. Region-based image registration can achieve high-precision registration of heterogeneous images. However, it performs poorly for images with large rotations, scale differences, and it has heavy computation task. For these problem, this paper combines the advantages of feature-based and region-based image registration methods into a hybrid model and proposes an automatic registration algorithm for optical and SAR images. The optical remote sensing image is the reference image while the SAR image is the one to be registered. The SAR-SIFT based on the feature points is used to complete the coarse registration and then the ROEWA-HOG based on the region is used to complete the fine registration. Firstly, the SAR-SIFT algorithm, robust to nonlinear intensity differences and speckle noise, is used to perform feature point detection and feature matching to calculate the affine transformation model of the image to eliminate the obvious rotation, scale and translation difference between the optical image and the SAR image. This is the coarse image registration. Secondly, we use the block Harris corner detection method to obtain a certain number of evenly distributed feature points on the reference image. We determine the search area of the corresponding points on the image to be registered according to the feature points, calculate the ROEWA gradient of the image, and then use a fast calculation strategy to construct the HOG feature vector in the template area with the feature points as the center. Then, we use SSD as the similarity measure to search the corresponding points on the image to be registered. This is the high-precision image registration. Finally, we carry out the image registration and perform visual inspection and quantitative evaluation of the registration results. It is demonstrated that the algorithm in this paper can combine the advantages of feature-based and region-based image registration methods to better resist the noise effect of SAR images and the nonlinear intensity, rotation, scale, and translation differences between optic and SAR images. The final registration accuracy of our high-precision automatic registration method is 1 pixel. High-precision automatic registration for SAR images can meet subsequent comprehensive applications of optical and SAR images.
ZHANG Mingxiang , WANG Zegen , BAI Ruyue , JIA Hongshun . A Coarse-to-Fine Optical and SAR Remote Sensing Image Registration Algorithm[J]. Journal of Geo-information Science, 2020 , 22(11) : 2238 -2246 . DOI: 10.12082/dqxxkx.2020.190742
表1 光学与SAR图像数据信息Tab.1 Optical and SAR image data information |
实验编号 | 成像传感器 | 图像尺寸/像元 | 地面分辨率 | 成像模式 | 入射角/° | 极化方式 | 产品级别 | 成像日期 | 成像区域 | ||
---|---|---|---|---|---|---|---|---|---|---|---|
实验1 | Googleearth | 1 m | - | - | - | - | 2012-05-07 | 挪威 | |||
TerraSAR-X | 1 m | ST | HH | Level-1B | 2013-04-10 | 挪威 | |||||
实验2 | Sentinel-2A 第4波段 | 10 m | - | - | - | Level-1C | 2018-06-05 | 运城 | |||
Sentinel-1A | 5×20 m | IW | VH | Level-1 | 2019-10-28 | 运城 |
表2 光学与SAR图像配准结果评估和比较Tab.2 Evaluation and comparison of optical and SAR image registration results |
配准算法 | 实验编号 | 粗配准RMSE | 精配准RMSE | 精配准CMR/% | 粗配准运行时间/s | 精配准运行时间/s |
---|---|---|---|---|---|---|
本文算法 | 实验1 | 5.35 | 1.21 | 81 | 39 | 109 |
实验2 | 3.83 | 0.91 | 93 | 53 | 147 | |
SAR-SIFT+HOPC | 实验1 | 5.43 | 1.09 | 89 | 41 | 231 |
实验2 | 3.85 | 0.82 | 95 | 51 | 318 |
[1] |
郭唯娜, 柯长青, 范宇宾. 基于SAR干涉数据的东帕米尔高原冰川变化[J]. 地球信息科学学报, 2019,21(11):1790-1801.
[
|
[2] |
叶沅鑫, 郝思媛, 曹云刚. 基于几何结构属性的光学和SAR影像自动配准[J]. 红外与毫米波学报, 2017,36(6):720-726.
[
|
[3] |
姜文聪, 张继贤, 程春泉, 等. SIFT与粗差剔除算法相结合的机载SAR影像匹配研究[J]. 地球信息科学学报, 2013,15(3):440-445.
[
|
[4] |
杨勇, 胡思茹. 基于模板匹配约束下的光学与SAR图像配准[J]. 系统工程与电子技术, 2019,41(10):2235-2242.
[
|
[5] |
|
[6] |
|
[7] |
|
[8] |
|
[9] |
|
[10] |
|
[11] |
|
[12] |
|
[13] |
|
[14] |
|
[15] |
|
[16] |
|
[17] |
|
[18] |
|
/
〈 |
|
〉 |