An automatic registration method for different temporal remote sensing images based on improved Fourier-Mellin algorithm

2009 ◽  
Author(s):  
Rui Li ◽  
Jiulin Sun ◽  
Fang Yin ◽  
Fusheng Guo ◽  
Wantong Wang
Keyword(s):  
Remote Sensing ◽  
Automatic Registration ◽  
Remote Sensing Images ◽  
Registration Method

scholarly journals UAV Remote Sensing Image Automatic Registration Based on Deep Residual Features

2021 ◽  
Vol 13 (18) ◽  
pp. 3605
Author(s):  
Xin Luo ◽  
Guangling Lai ◽  
Xiao Wang ◽  
Yuwei Jin ◽  
Xixu He ◽  
...  
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Rapid Development ◽  
Image Features ◽  
Automatic Registration ◽  
Remote Sensing Images ◽  
Registration Accuracy ◽  
Registration Method ◽  
Uav Images ◽  
High Level

With the rapid development of unmanned aerial vehicle (UAV) technology, UAV remote sensing images are increasing sharply. However, due to the limitation of the perspective of UAV remote sensing, the UAV images obtained from different viewpoints of a same scene need to be stitched together for further applications. Therefore, an automatic registration method of UAV remote sensing images based on deep residual features is proposed in this work. It needs no additional training and does not depend on image features, such as points, lines and shapes, or on specific image contents. This registration framework is built as follows: Aimed at the problem that most of traditional registration methods only use low-level features for registration, we adopted deep residual neural network features extracted by an excellent deep neural network, ResNet-50. Then, a tensor product was employed to construct feature description vectors through exacted high-level abstract features. At last, the progressive consistency algorithm (PROSAC) was exploited to remove false matches and fit a geometric transform model so as to enhance registration accuracy. The experimental results for different typical scene images with different resolutions acquired by different UAV image sensors indicate that the improved algorithm can achieve higher registration accuracy than a state-of-the-art deep learning registration algorithm and other popular registration algorithms.

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.

High accuracy-automatic registration method research on multi-source remote sensing image

2016 ◽  
Vol 31 (6) ◽  
pp. 604-612
Author(s):  
程国华 CHENG Guo-hua ◽  
王阿川 WANG a-chuan ◽  
陈舒畅 CHEN Shu-chang ◽  
赵 宇 ZHAO Yu ◽  
范晓锐 FAN Xiao-rui ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Image ◽  
High Accuracy ◽  
Automatic Registration ◽  
Registration Method
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