scholarly journals A NORMALIZED SURF FOR MULTISPECTRAL IMAGE MATCHING AND BAND CO-REGISTRATION

2019 ◽  
Vol XLII-2/W13 ◽  
pp. 393-399 ◽  
Author(s):  
J. P. Jhan ◽  
J. Y. Rau
Keyword(s):  
Image Registration ◽  
Image Matching ◽  
Spectral Response ◽  
Scale Space ◽  
Local Extremum ◽  
Transformation Model ◽  
Cumulative Distribution ◽  
Geometric Transformation ◽  
Registration Accuracy ◽  
The Difference

<p><strong>Abstract.</strong> Due to the raw images of multi-lens multispectral (MS) camera has significant misregistration errors, performing image registration for band co-registration is necessary. Image matching is an essential step for image registration, which obtains conjugate features on the overlapped areas, and use them to estimate the coefficients of a transformation model for correcting the geometrical errors. However, due to the none-linear intensity of spectral response, performing feature-based image matching (such as SURF) can only obtain only a few conjugate features on cross-band MS images. Different to SURF that extracts local extremum in a multi-scale space and utilizes a threshold to determine a feature, we proposed a normalized SURF (N-SURF) that extracts features on single scale, calculates the cumulative distribution function (CDF) of features, and obtains consistent features from the CDF. In this study, two datasets acquired from Tetracam MiniMCA-12 and Micasense RedEdge Altum are used for evaluating the matching performance of N-SURF. Results show that N-SURF can extract approximately 2&amp;ndash;3 times number of features, match more points, and have more efficient than original SURF. On the other hand, with the successful of MS image matching, we can therefor use the conjugates to compute the coefficients of a geometric transformation model. In this study, three transformation models are used to compare the difference on MS band co-registration, i.e. affine, projective, and extended projective. Results show that extended projective model is better than the others as it can compensate the difference of lens distortion and viewpoint, and has co-registration accuracy of 0.3&amp;ndash;0.6 pixels.</p>

scholarly journals Feedback Unilateral Grid-Based Clustering Feature Matching for Remote Sensing Image Registration

2019 ◽  
Vol 11 (12) ◽  
pp. 1418
Author(s):  
Zhaohui Zheng ◽  
Hong Zheng ◽  
Yong Ma ◽  
Fan Fan ◽  
Jianping Ju ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Image Registration ◽  
Image Matching ◽  
Feature Matching ◽  
Remote Sensing Image ◽  
Registration Accuracy ◽  
Computation Efficiency ◽  
Feature Based ◽  
Local Grid ◽  
Grid Based

In feature-based image matching, implementing a fast and ultra-robust feature matching technique is a challenging task. To solve the problems that the traditional feature matching algorithm suffers from, such as long running time and low registration accuracy, an algorithm called feedback unilateral grid-based clustering (FUGC) is presented which is able to improve computation efficiency, accuracy and robustness of feature-based image matching while applying it to remote sensing image registration. First, the image is divided by using unilateral grids and then fast coarse screening of the initial matching feature points through local grid clustering is performed to eliminate a great deal of mismatches in milliseconds. To ensure that true matches are not erroneously screened, a local linear transformation is designed to take feedback verification further, thereby performing fine screening between true matching points deleted erroneously and undeleted false positives in and around this area. This strategy can not only extract high-accuracy matching from coarse baseline matching with low accuracy, but also preserves the true matching points to the greatest extent. The experimental results demonstrate the strong robustness of the FUGC algorithm on various real-world remote sensing images. The FUGC algorithm outperforms current state-of-the-art methods and meets the real-time requirement.

Combining Feature Correspondence with Parametric Chamfer Alignment: Hybrid Two-Stage Registration for Ultra-Widefield Retinal Images

2021 ◽  
Author(s):  
Li Ding ◽  
Tony Kang ◽  
Ajay E. Kuriyan ◽  
Rajeev S. Ramchandran ◽  
Charles C. Wykoff ◽  
...  
Keyword(s):  
Image Registration ◽  
Fluorescein Angiography ◽  
Geometric Transformation ◽  
Retinal Images ◽  
Local Refinement ◽  
Registration Accuracy ◽  
Feature Correspondence ◽  
Geometric Distortions ◽  
Global Registration ◽  
Large Sets

<div>We propose a novel hybrid framework for registering retinal images in the presence of extreme geometric distortions that are commonly encountered in ultra-widefield (UWF) fluorescein angiography. Our approach consists of two stages: a feature-based global registration and a vessel-based local refinement. For the global registration, we introduce a modified RANSAC algorithm that jointly identifies robust matches between feature keypoints in reference and target images and estimates a polynomial geometric transformation consistent with the identified correspondences. Our RANSAC modification particularly improves feature point matching and the registration in peripheral regions that are most severely impacted by the geometric distortions. The second local refinement stage is formulated in our framework as a parametric chamfer alignment for vessel maps obtained using a deep neural network. Because the complete vessel maps contribute to the chamfer alignment, this approach not only improves registration accuracy but also aligns with clinical practice, where vessels are typically a key focus of examinations. We validate the effectiveness of the proposed framework on a new UWF fluorescein angiography (FA) dataset and on the existing narrow-field FIRE (fundus image registration) dataset and demonstrate that it significantly outperforms prior retinal image registration methods. The proposed approach enhances the utility of large sets of longitudinal UWF images by enabling: (a) automatic computation of vessel change metrics and (b) standardized and co-registered examination that can better highlight changes of clinical interest to physicians.</div>

An Image Registration Method Combing Feature Constraint with Multilevel Strategy

2011 ◽  
Vol 58-60 ◽  
pp. 286-291
Author(s):  
Hong Kui Xu ◽  
Ming Yan Jiang ◽  
Ming Qiang Yang
Keyword(s):  
Image Registration ◽  
Optimization Methods ◽  
Local Extremum ◽  
Parametric Image ◽  
Registration Accuracy ◽  
Registration Method ◽  
Structure Information ◽  
Local Difference ◽  
Intelligent Optimization Algorithms ◽  
Image Registrations

A novel method combing feature constraint with multilevel strategy to improve simultaneously the registration accuracy and speed is proposed for non-parametric image registrations. To images between which the local difference is large, integrating feature constraint constructed with local structure information of images into objective function of image registration improves the registration accuracy. When applying feature constraint under multilevel strategy, parameter searching is prevented from entrapped into local extremum by using the optimization result on coarser levels as the starting points on finer levels; meanwhile traditional optimization methods without demanding intelligent optimization algorithms which consume more time can find the accurate registration parameter on finer levels, so registration speed is improved. Experimental results indicate that this method can finish fast and accurate registration for images between which there exists large local difference.

scholarly journals A PRELIMINARY INVESTIGATION ON COMPARISON AND TRANSFORMATION OF SENTINEL-2 MSI AND LANDSAT 8 OLI

2018 ◽  
Vol XLII-3 ◽  
pp. 2619-2624 ◽  
Author(s):  
F. Chen ◽  
S. Lou ◽  
Q. Fan ◽  
J. Li ◽  
C. Wang ◽  
...  
Keyword(s):  
Linear Transformation ◽  
Spectral Response ◽  
Preliminary Investigation ◽  
Transformation Model ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Linear Transformation Model ◽  
Data Archives ◽  
The Difference ◽  
Sentinel 2

A PRELIMINARY INVESTIGATION ON COMPARISON AND TRANSFORMATION OF SENTINEL-2 MSI AND LANDSAT 8 OLI Timely and accurate earth observation with short revisit interval is usually necessary, especially for emergency response. Currently, several new generation sensors provided with similar channel characteristics have been operated onboard different satellite platforms, including Sentinel-2 and Landsat 8. Joint use of the observations by different sensors offers an opportunity to meet the demands for emergency requirements. For example, through the combination of Landsat and Sentinel-2 data, the land can be observed every 2&amp;ndash;3 days at medium spatial resolution. However, differences are expected in radiometric values (e.g., channel reflectance) of the corresponding channels between two sensors. Spectral response function (SRF) is taken as an important aspect of sensor settings. Accordingly, between-sensor differences due to SRFs variation need to be quantified and compensated. The comparison of SRFs shows difference (more or less) in channel settings between Sentinel-2 Multi-Spectral Instrument (MSI) and Landsat 8 Operational Land Imager (OLI). Effect of the difference in SRF on corresponding values between MSI and OLI was investigated, mainly in terms of channel reflectance and several derived spectral indices. Spectra samples from ASTER Spectral Library Version 2.0 and Hyperion data archives were used in obtaining channel reflectance simulation of MSI and OLI. Preliminary results show that MSI and OLI are well comparable in several channels with small relative discrepancy (&amp;lt;&amp;thinsp;5&amp;thinsp;%), including the Costal Aerosol channel, a NIR (855&amp;ndash;875&amp;thinsp;nm) channel, the SWIR channels, and the Cirrus channel. Meanwhile, for channels covering Blue, Green, Red, and NIR (785&amp;ndash;900&amp;thinsp;nm), the between-sensor differences are significantly presented. Compared with the difference in reflectance of each individual channel, the difference in derived spectral index is more significant. In addition, effectiveness of linear transformation model is not ensured when the target belongs to another spectra collection. If an improper transformation model is selected, the between-sensor discrepancy will even largely increase. In conclusion, improvement in between-sensor consistency is possibly a challenge, through linear transformation based on model(s) generated from other spectra collections.

scholarly journals Combining Feature Correspondence with Parametric Chamfer Alignment: Hybrid Two-Stage Registration for Ultra-Widefield Retinal Images

2021 ◽  
Author(s):  
Li Ding ◽  
Tony Kang ◽  
Ajay E. Kuriyan ◽  
Rajeev S. Ramchandran ◽  
Charles C. Wykoff ◽  
...  
Keyword(s):  
Image Registration ◽  
Fluorescein Angiography ◽  
Geometric Transformation ◽  
Retinal Images ◽  
Local Refinement ◽  
Registration Accuracy ◽  
Feature Correspondence ◽  
Geometric Distortions ◽  
Global Registration ◽  
Large Sets

<div>We propose a novel hybrid framework for registering retinal images in the presence of extreme geometric distortions that are commonly encountered in ultra-widefield (UWF) fluorescein angiography. Our approach consists of two stages: a feature-based global registration and a vessel-based local refinement. For the global registration, we introduce a modified RANSAC algorithm that jointly identifies robust matches between feature keypoints in reference and target images and estimates a polynomial geometric transformation consistent with the identified correspondences. Our RANSAC modification particularly improves feature point matching and the registration in peripheral regions that are most severely impacted by the geometric distortions. The second local refinement stage is formulated in our framework as a parametric chamfer alignment for vessel maps obtained using a deep neural network. Because the complete vessel maps contribute to the chamfer alignment, this approach not only improves registration accuracy but also aligns with clinical practice, where vessels are typically a key focus of examinations. We validate the effectiveness of the proposed framework on a new UWF fluorescein angiography (FA) dataset and on the existing narrow-field FIRE (fundus image registration) dataset and demonstrate that it significantly outperforms prior retinal image registration methods. The proposed approach enhances the utility of large sets of longitudinal UWF images by enabling: (a) automatic computation of vessel change metrics and (b) standardized and co-registered examination that can better highlight changes of clinical interest to physicians.</div>

scholarly journals Detection of vessel bifurcations in CT scans for automatic objective assessment of deformable image registration accuracy

2021 ◽  
Author(s):  
Guillaume Cazoulat ◽  
Brian M Anderson ◽  
Molly M McCulloch ◽  
Bastien Rigaud ◽  
Eugene J Koay ◽  
...  
Keyword(s):  
Image Registration ◽  
Objective Assessment ◽  
Deformable Image Registration ◽  
Ct Scans ◽  
Registration Accuracy ◽  
Vessel Bifurcations

scholarly journals Exploring the Use of PlanetScope Data for Particulate Matter Air Quality Research

2021 ◽  
Vol 13 (15) ◽  
pp. 2981
Author(s):  
Jeanné le Roux ◽  
Sundar Christopher ◽  
Manil Maskey
Keyword(s):  
Particulate Matter ◽  
Air Quality ◽  
Land Cover ◽  
Spectral Response ◽  
Fine Particulate Matter ◽  
Daily Basis ◽  
Land Cover Types ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
The Difference ◽  
Geolocation Accuracy

Planet, a commercial company, has achieved a key milestone by launching a large fleet of small satellites (smallsats) that provide high spatial resolution imagery of the entire Earth’s surface on a daily basis with its PlanetScope sensors. Given the potential utility of these data, this study explores the use for fine particulate matter (PM2.5) air quality applications. However, before these data can be utilized for air quality applications, key features of the data, including geolocation accuracy, calibration quality, and consistency in spectral signatures, need to be addressed. In this study, selected Dove-Classic PlanetScope data is screened for geolocation consistency. The spectral response of the Dove-Classic PlanetScope data is then compared to Moderate Resolution Imaging Spectroradiometer (MODIS) data over different land cover types, and under varying PM2.5 and mid visible aerosol optical depth (AOD) conditions. The data selected for this study was found to fall within Planet’s reported geolocation accuracy of 10 m (between 3–4 pixels). In a comparison of top of atmosphere (TOA) reflectance over a sample of different land cover types, the difference in reflectance between PlanetScope and MODIS ranged from near-zero (0.0014) to 0.117, with a mean difference in reflectance of 0.046 ± 0.031 across all bands. The reflectance values from PlanetScope were higher than MODIS 78% of the time, although no significant relationship was found between surface PM2.5 or AOD and TOA reflectance for the cases that were studied. The results indicate that commercial satellite data have the potential to address Earth-environmental issues.

Progressive Large Scale-Invariant Image Matching in Scale Space

2017 ◽  
Author(s):  
Lei Zhou ◽  
Siyu Zhu ◽  
Tianwei Shen ◽  
Jinglu Wang ◽  
Tian Fang ◽  
...  
Keyword(s):  
Image Matching ◽  
Large Scale ◽  
Scale Space ◽  
Scale Invariant

Theoretical study on photoemission of double-layer AlxGa1−xAs/GaAs nanocone array photocathode

2021 ◽  
Author(s):  
Yan Sun ◽  
Lei Liu ◽  
Zhisheng Lv ◽  
Xingyue Zhangyang ◽  
Feifei Lu ◽  
...  
Keyword(s):  
Cross Section ◽  
Light Trapping ◽  
Spectral Response ◽  
Circular Cross Section ◽  
Geometrical Parameters ◽  
Internal Electric Field ◽  
Surface Emission ◽  
The Difference ◽  
Section Structure ◽  
Light Capture

In the design of photocathode, the internal electric field could be formed due to the graded Al compositional [Formula: see text] nanostructure, which can improve the top surface emission probability of carriers. In this paper, [Formula: see text] nanostructure array photocathode composed of two sub-layers is presented. Based on the finite element method, the influence of graded geometrical parameters on their optoelectronic characteristics is investigated. The results show that when the thickness of the sublayer is equal, the difference of the Al composition between the two sublayers of nanostructure is larger, the sub-layers are less, and the quantum efficiency is higher. The light capture ability of the photocathode can be enhanced by increasing the thickness and the array spacing of the first sublayer. Compared with the hexagonal cross-section structure, the light trapping effect and spectral response of the circular cross-section structure are better.

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