scholarly journals Well-Distributed Feature Extraction for Image Registration Using Histogram Matching

2019 ◽  
Vol 9 (17) ◽  
pp. 3487 ◽  
Author(s):  
Muhammad Tariq Mahmood ◽  
Ik Hyun Lee
Keyword(s):  
High Resolution ◽  
Image Registration ◽  
Satellite Image ◽  
Computational Cost ◽  
Control Points ◽  
Scale Invariant ◽  
Registration Method ◽  
Histogram Matching ◽  
Spatial Alignment ◽  
High Resolution Satellite Imagery

Image registration is a spatial alignment of corresponding images of the same scene acquired from different views, sensors, and time intervals. Especially, satellite image registration is a challenging task due to the high resolution of images. In addition, demands for high resolution satellite imagery are increased for more detailed and precise information in land planning, urban planning, and Earth observation. Commonly, feature-based methods are applied for image registration. In these methods, first control or key points are detected using feature detector such as scale-invariant feature transform (SIFT). The numbers and the distribution of these control points are important for the remaining steps of registration. These methods provide reasonable performance; however, they suffer from high computational cost and irregular distribution of control points. To overcome these limitations, we propose an area-based registration method using histogram matching and zero mean normalized cross-correlation (ZNCC). In multi-spectral satellite images, first, different spectral responses are adjusted by using histogram matching. Then, ZNCC is utilized to extract well-distributed control points. In addition, fast Fourier transform (FFT) and block-wise processing are applied to reduce the computational cost. The proposed method is evaluated through various input datasets. The results demonstrate its efficacy and accuracy in image registration.

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.

EFFICIENT INVARIANT IMAGE REGISTRATION UTILIZING PRE-SHIFTED LOGARITHMIC SPIRAL

2010 ◽  
Vol 10 (03) ◽  
pp. 395-421
Author(s):  
RITTAVEE MATUNGKA ◽  
YUAN F. ZHENG ◽  
ROBERT L. EWING
Keyword(s):  
Image Registration ◽  
Visual Information ◽  
Computational Cost ◽  
Logarithmic Spiral ◽  
Sampling Point ◽  
Scale Invariant ◽  
Point Distribution ◽  
Registration Method ◽  
Gabor Feature ◽  
Gabor Feature Extraction

Image registration is an essential step in many image processing applications that need visual information from multiple images for comparison, integration or analysis. Recently researchers have introduced image registration techniques using the log-polar transform (LPT) for its rotation and scale invariant properties. However, there are two major problems with the LPT based image registration method: inefficient sampling point distribution and high computational cost in the matching procedure. Motivated by the success of LPT based approach, we propose a novel pre-shifted logarithmic spiral (PSLS) approach that distributes the sampling point more efficiently, robust to translation, scale, and rotation. By pre-shifting the sampling point by π/nθ radian, the total number of samples in the angular direction can be reduced by half. This yields great reduction in computational load in the matching process. Translation between the registered images is recovered with the new search scheme using Gabor feature extraction to accelerate the localization procedure. Experiments on real images demonstrate the effectiveness and robustness of the proposed approach for registering images that are subjected to scale, rotation and translation.

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.

scholarly journals AUTOMATIC BLOCKED ROADS ASSESSMENT AFTER EARTHQUAKE USING HIGH RESOLUTION SATELLITE IMAGERY

2015 ◽  
Vol XL-1-W5 ◽  
pp. 601-605 ◽  
Author(s):  
H. Rastiveis ◽  
E. Hosseini-Zirdoo ◽  
F. Eslamizade
Keyword(s):  
High Resolution ◽  
World View ◽  
Satellite Image ◽  
Data Set ◽  
The Road ◽  
Vector Map ◽  
Processing Step ◽  
Image Quality Improvement ◽  
High Resolution Satellite Imagery ◽  
The City

In 2010, an earthquake in the city of Port-au-Prince, Haiti, happened quite by chance an accident and killed over 300000 people. According to historical data such an earthquake has not occurred in the area. Unpredictability of earthquakes has necessitated the need for comprehensive mitigation efforts to minimize deaths and injuries. Blocked roads, caused by debris of destroyed buildings, may increase the difficulty of rescue activities. In this case, a damage map, which specifies blocked and unblocked roads, can be definitely helpful for a rescue team. <br><br> In this paper, a novel method for providing destruction map based on pre-event vector map and high resolution world view II satellite images after earthquake, is presented. For this purpose, firstly in pre-processing step, image quality improvement and co-coordination of image and map are performed. Then, after extraction of texture descriptor from the image after quake and SVM classification, different terrains are detected in the image. Finally, considering the classification results, specifically objects belong to “debris” class, damage analysis are performed to estimate the damage percentage. In this case, in addition to the area objects in the “debris” class their shape should also be counted. The aforementioned process are performed on all the roads in the road layer.In this research, pre-event digital vector map and post-event high resolution satellite image, acquired by Worldview-2, of the city of Port-au-Prince, Haiti's capital, were used to evaluate the proposed method. The algorithm was executed on 1200×800 m2 of the data set, including 60 roads, and all the roads were labelled correctly. The visual examination have authenticated the abilities of this method for damage assessment of urban roads network after an earthquake.

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