A robust registration method for high resolution remote sensing images

2008 ◽  
Author(s):  
Yingdan Wu ◽  
Yang Ming
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Remote Sensing Images ◽  
Registration Method

scholarly journals An Image Registration Method Using Deep Residual Network Features for Multisource High-Resolution Remote Sensing Images

2021 ◽  
Vol 13 (17) ◽  
pp. 3425
Author(s):  
Xin Zhao ◽  
Hui Li ◽  
Ping Wang ◽  
Linhai Jing
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Image Registration ◽  
Feature Descriptor ◽  
Remote Sensing Images ◽  
Residual Network ◽  
Scale Invariant ◽  
Registration Method ◽  
Sensing Applications ◽  
Feature Based

Accurate registration for multisource high-resolution remote sensing images is an essential step for various remote sensing applications. Due to the complexity of the feature and texture information of high-resolution remote sensing images, especially for images covering earthquake disasters, feature-based image registration methods need a more helpful feature descriptor to improve the accuracy. However, traditional image registration methods that only use local features at low levels have difficulty representing the features of the matching points. To improve the accuracy of matching features for multisource high-resolution remote sensing images, an image registration method based on a deep residual network (ResNet) and scale-invariant feature transform (SIFT) was proposed. It used the fusion of SIFT features and ResNet features on the basis of the traditional algorithm to achieve image registration. The proposed method consists of two parts: model construction and training and image registration using a combination of SIFT and ResNet34 features. First, a registration sample set constructed from high-resolution satellite remote sensing images was used to fine-tune the network to obtain the ResNet model. Then, for the image to be registered, the Shi_Tomas algorithm and the combination of SIFT and ResNet features were used for feature extraction to complete the image registration. Considering the difference in image sizes and scenes, five pairs of images were used to conduct experiments to verify the effectiveness of the method in different practical applications. The experimental results showed that the proposed method can achieve higher accuracies and more tie points than traditional feature-based methods.

scholarly journals A novel registration method for high resolution remote sensing images based on JSEG and NMI

2015 ◽  
Vol 12 (1) ◽  
pp. 289-306 ◽  
Author(s):  
Chao Wang ◽  
Aiye Shi ◽  
Xin Wang ◽  
Fengchen Huang ◽  
Hui Liu
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Image Registration ◽  
Control Point ◽  
Remote Sensing Images ◽  
Registration Method ◽  
Multi Scale ◽  
Feature Point Extraction ◽  
First Time ◽  
Selection Of

When traditional multi-scale analysis tools are applied to high resolution remote sensing image registration, difficulties and limitations are common in selection of directional sub-bands and distribution optimization of control point pairs etc. Aiming at this issue, a novel registration method based on JSEG and NMI is proposed in this paper. It is the method that incorporates the multi-scale segmentation method (JSEG) into image registration for the first time and proposes an adaptive feature point extraction method on the basis of blocking strategy. Then, NMI is adopted to obtain a set of control point pairs. Finally, the image registration is realized by virtue of Delaunay triangle local transform mapping functions. In accordance with experiments on high resolution remote sensing images collected by different sensors, it is found that the method can not only extract feature points accurately but also ensure reasonable distribution of control point pairs. Meanwhile, compared with traditional multi-scale tools-based methods, the method has relatively high accuracy and robustness.

scholarly journals An Image Registration Method for Multisource High-Resolution Remote Sensing Images for Earthquake Disaster Assessment

Sensors ◽  
2020 ◽  
Vol 20 (8) ◽  
pp. 2286 ◽  
Author(s):  
Xin Zhao ◽  
Hui Li ◽  
Ping Wang ◽  
Linhai Jing
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Image Registration ◽  
Greedy Algorithms ◽  
Corner Detection ◽  
Remote Sensing Images ◽  
Scale Invariant ◽  
Registration Method ◽  
Earthquake Disaster ◽  
Disaster Assessment

For earthquake disaster assessment using remote sensing (RS), multisource image registration is an important step. However, severe earthquakes will increase the deformation between the remote sensing images acquired before and after the earthquakes on different platforms. Traditional image registration methods can hardly meet the requirements of accuracy and efficiency of image registration of post-earthquake RS images used for disaster assessment. Therefore, an improved image registration method was proposed for the registration of multisource high-resolution remote sensing images. The proposed method used the combination of the Shi_Tomasi corner detection algorithm and scale-invariant feature transform (SIFT) to detect tie points from image patches obtained by an image partition strategy considering geographic information constraints. Then, the random sample consensus (RANSAC) and greedy algorithms were employed to remove outliers and redundant matched tie points. Additionally, a pre-earthquake RS image database was constructed using pre-earthquake high-resolution RS images and used as the references for image registration. The performance of the proposed method was evaluated using three image pairs covering regions affected by severe earthquakes. It was shown that the proposed method provided higher accuracy, less running time, and more tie points with a more even distribution than the classic SIFT method and the SIFT method using the same image partitioning strategy.

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