A novel compensation algorithm of aerial image registration

2017 ◽  
Author(s):  
Wei Han ◽  
Daqing Huang ◽  
Cheng Xu
Keyword(s):  
Image Registration ◽  
Aerial Image ◽  
Compensation Algorithm

Reducing Shadow Effects on the Co-Registration of Aerial Image Pairs

2020 ◽  
Vol 86 (3) ◽  
pp. 177-186
Author(s):  
Matthew Plummer ◽  
Douglas Stow ◽  
Emanuel Storey ◽  
Lloyd Coulter ◽  
Nicholas Zamora ◽  
...  
Keyword(s):  
Image Registration ◽  
Image Data ◽  
Aerial Image ◽  
Registration Accuracy ◽  
Illumination Variation ◽  
Scale Invariant ◽  
Match Point ◽  
Linear Correction ◽  
Multi Temporal ◽  
Image Pairs

Image registration is an important preprocessing step prior to detecting changes using multi-temporal image data, which is increasingly accomplished using automated methods. In high spatial resolution imagery, shadows represent a major source of illumination variation, which can reduce the performance of automated registration routines. This study evaluates the statistical relationship between shadow presence and image registration accuracy, and whether masking and normalizing shadows leads to improved automatic registration results. Eighty-eight bitemporal aerial image pairs were co-registered using software called Scale Invariant Features Transform (<small>SIFT</small>) and Random Sample Consensus (<small>RANSAC</small>) Alignment (<small>SARA</small>). Co-registration accuracy was assessed at different levels of shadow coverage and shadow movement within the images. The primary outcomes of this study are (1) the amount of shadow in a multi-temporal image pair is correlated with the accuracy/success of automatic co-registration; (2) masking out shadows prior to match point select does not improve the success of image-to-image co-registration; and (3) normalizing or brightening shadows can help match point routines find more match points and therefore improve performance of automatic co-registration. Normalizing shadows via a standard linear correction provided the most reliable co-registration results in image pairs containing substantial amounts of relative shadow movement, but had minimal effect for pairs with stationary shadows.

A HYBRID AND HIERARCHICAL APPROACH TO AERIAL IMAGE REGISTRATION

2007 ◽  
Vol 21 (03) ◽  
pp. 573-590 ◽  
Author(s):  
DONGJIANG XU ◽  
TAKIS KASPARIS
Keyword(s):  
Image Registration ◽  
Hybrid Approach ◽  
Correlation Method ◽  
Estimation Algorithm ◽  
Flow Equation ◽  
Phase Correlation ◽  
Aerial Images ◽  
Aerial Image ◽  
Common Information ◽  
Coarse To Fine

This paper proposes a hybrid approach to image registration for inferring the affine transformation that best matches a pair of partially overlapping aerial images. The image registration is formulated as a two-stage hybrid approach combining both phase correlation method (PCME) and optical flow equation (OFE) based estimation algorithm in a coarse-to-fine manner. With PCME applied at the highest level of decomposition, the initial affine parameter model could be first estimated. Subsequently, the OFE-based estimation algorithm is incorporated into the proposed hybrid approach using a multi-resolution mechanism. PCME is characterized by its insensitivity to large geometric transform between images, which can effectively guide the OFE-based registration. For image pairs under salient brightness variations, we propose a nonlinear image representation that emphasizes common intensity information, suppresses the non-common information between an image pair, and is suitable for the proposed coarse-to-fine hierarchical iterative processing. Experimental results demonstrate the accuracy and efficiency of our proposed approach using different types of aerial images.

Aerial Image Registration for Tracking

2015 ◽  
Vol 53 (4) ◽  
pp. 2137-2145 ◽  
Author(s):  
Michael E. Linger ◽  
A. Ardeshir Goshtasby
Keyword(s):  
Image Registration ◽  
Aerial Image
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