Research and implementation of high resolution remote sensing image registration method

2017 ◽  
Author(s):  
Wan-zhen Zhou ◽  
Qiu-xiao Li
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Image Registration ◽  
Remote Sensing Image ◽  
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.

Multilevel similarity model for high-resolution remote sensing image registration

2019 ◽  
Vol 505 ◽  
pp. 294-305 ◽  
Author(s):  
Xianmin Wang ◽  
Jing Li ◽  
Jin Li ◽  
Hongyang Yan
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Image Registration ◽  
Remote Sensing Image ◽  
Similarity Model

scholarly journals High-Resolution Optical Remote Sensing Image Registration via Reweighted Random Walk based Hyper-Graph Matching

2019 ◽  
Vol 11 (23) ◽  
pp. 2841 ◽  
Author(s):  
Wu ◽  
Di ◽  
Ming ◽  
Lv ◽  
Tan
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Image Registration ◽  
Graph Matching ◽  
Remote Sensing Image ◽  
Feature Point ◽  
Optical Remote Sensing ◽  
Scale Invariant ◽  
Matching Algorithm ◽  
Optical Remote Sensing Image

High-resolution optical remote sensing image registration is still a challenging task due to non-linearity in the intensity differences and geometric distortion. In this paper, an efficient method utilizing a hyper-graph matching algorithm is proposed, which can simultaneously use the high-order structure information and radiometric information, to obtain thousands of feature point pairs for accurate image registration. The method mainly consists of the following steps: firstly, initial matching by Uniform Robust Scale-Invariant Feature Transform (UR-SIFT) is carried out in the highest pyramid image level to derive the approximate geometric relationship between the images; secondly, two-stage point matching is performed to find the matches, that is, a rotation and scale invariant area-based matching method is used to derive matching candidates for each feature point and an efficient hyper-graph matching algorithm is applied to find the best match for each feature point; thirdly, a local quadratic polynomial constraint framework is used to eliminate match outliers; finally, the above process is iterated until finishing the matching in the original image. Then, the obtained correspondences are used to perform the image registration. The effectiveness of the proposed method is tested with six pairs of high-resolution optical images, covering different landscape types—such as mountain area, urban, suburb, and flat land—and registration accuracy of sub-pixel level is obtained. The experiments show that the proposed method outperforms the conventional matching algorithms such as SURF, AKAZE, ORB, BRISK, and FAST in terms of total number of correct matches and matching precision.

scholarly journals Modality-Free Feature Detector and Descriptor for Multimodal Remote Sensing Image Registration

2020 ◽  
Vol 12 (18) ◽  
pp. 2937
Author(s):  
Song Cui ◽  
Miaozhong Xu ◽  
Ailong Ma ◽  
Yanfei Zhong
Keyword(s):  
Remote Sensing ◽  
Image Registration ◽  
Near Infrared ◽  
Feature Matching ◽  
Simulated Data ◽  
Remote Sensing Image ◽  
Surface Model ◽  
Phase Congruency ◽  
Remote Sensing Images ◽  
Registration Method

The nonlinear radiation distortions (NRD) among multimodal remote sensing images bring enormous challenges to image registration. The traditional feature-based registration methods commonly use the image intensity or gradient information to detect and describe the features that are sensitive to NRD. However, the nonlinear mapping of the corresponding features of the multimodal images often results in failure of the feature matching, as well as the image registration. In this paper, a modality-free multimodal remote sensing image registration method (SRIFT) is proposed for the registration of multimodal remote sensing images, which is invariant to scale, radiation, and rotation. In SRIFT, the nonlinear diffusion scale (NDS) space is first established to construct a multi-scale space. A local orientation and scale phase congruency (LOSPC) algorithm are then used so that the features of the images with NRD are mapped to establish a one-to-one correspondence, to obtain sufficiently stable key points. In the feature description stage, a rotation-invariant coordinate (RIC) system is adopted to build a descriptor, without requiring estimation of the main direction. The experiments undertaken in this study included one set of simulated data experiments and nine groups of experiments with different types of real multimodal remote sensing images with rotation and scale differences (including synthetic aperture radar (SAR)/optical, digital surface model (DSM)/optical, light detection and ranging (LiDAR) intensity/optical, near-infrared (NIR)/optical, short-wave infrared (SWIR)/optical, classification/optical, and map/optical image pairs), to test the proposed algorithm from both quantitative and qualitative aspects. The experimental results showed that the proposed method has strong robustness to NRD, being invariant to scale, radiation, and rotation, and the achieved registration precision was better than that of the state-of-the-art methods.

scholarly journals Remote Sensing Image Registration Using Multiple Image Features

2017 ◽  
Author(s):  
Kun Yang ◽  
Anning Pan ◽  
Yang Yang ◽  
Su Zhang ◽  
Sim Heng Ong ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Image Registration ◽  
Mixture Model ◽  
Damage Assessment ◽  
Remote Sensing Image ◽  
Image Features ◽  
Google Earth ◽  
Geometric Distortion ◽  
Remote Sensing Images ◽  
Registration Method

Remote sensing image registration plays an important role in military and civilian fields, such as natural disaster damage assessment, military damage assessment and ground targets identification, etc. However, due to the ground relief variations and imaging viewpoint changes, non-rigid geometric distortion occurs between remote sensing images with different viewpoint, which further increases the difficulty of remote sensing image registration. To address the problem, we propose a multi-viewpoint remote sensing image registration method which contains the following contributions. (i) A multiple features based finite mixture model is constructed for dealing with different types of image features. (ii) Three features are combined and substituted into the mixture model to form a feature complementation, i.e., the Euclidean distance and shape context are used to measure the similarity of geometric structure, and the SIFT (scale-invariant feature transform) distance which is endowed with the intensity information is used to measure the scale space extrema. (iii) To prevent the ill-posed problem, a geometric constraint term is introduced into the L2E-based energy function for better behaving the non-rigid transformation. We evaluated the performances of the proposed method by three series of remote sensing images obtained from the unmanned aerial vehicle (UAV) and Google Earth, and compared with five state-of-the-art methods where our method shows the best alignments in most cases.

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