scholarly journals ROBUST MOSAICKING OF UAV IMAGES WITH NARROW OVERLAPS

2016 ◽  
Vol XLI-B1 ◽  
pp. 879-883
Author(s):  
J. Kim ◽  
T. Kim ◽  
D. Shin ◽  
S. H. Kim
Keyword(s):  
Affine Transformation ◽  
Initial Approximation ◽  
Perspective Transformation ◽  
Relative Orientation ◽  
Transformation Model ◽  
Reference Image ◽  
General Transformation ◽  
Image Mosaicking ◽  
Uav Images ◽  
Relative Transformation

This paper considers fast and robust mosaicking of UAV images under a circumstance that each UAV images have very narrow overlaps in-between. Image transformation for image mosaicking consists of two estimations: relative transformations and global transformations. For estimating relative transformations between adjacent images, projective transformation is widely considered. For estimating global transformations, panoramic constraint is widely used. While perspective transformation is a general transformation model in 2D-2D transformation, this may not be optimal with weak stereo geometry such as images with narrow overlaps. While panoramic constraint works for reliable conversion of global transformation for panoramic image generation, this constraint is not applicable to UAV images in linear motions. For these reasons, a robust approach is investigated to generate a high quality mosaicked image from narrowly overlapped UAV images. For relative transformations, several transformation models were considered to ensure robust estimation of relative transformation relationship. Among them were perspective transformation, affine transformation, coplanar relative orientation, and relative orientation with reduced adjustment parameters. Performance evaluation for each transformation model was carried out. The experiment results showed that affine transformation and adjusted coplanar relative orientation were superior to others in terms of stability and accuracy. For global transformation, we set initial approximation by converting each relative transformation to a common transformation with respect to a reference image. In future work, we will investigate constrained relative orientation for enhancing geometric accuracy of image mosaicking and bundle adjustments of each relative transformation model for optimal global transformation.

scholarly journals ROBUST MOSAICKING OF UAV IMAGES WITH NARROW OVERLAPS

2016 ◽  
Vol XLI-B1 ◽  
pp. 879-883 ◽  
Author(s):  
J. Kim ◽  
T. Kim ◽  
D. Shin ◽  
S. H. Kim
Keyword(s):  
Affine Transformation ◽  
Initial Approximation ◽  
Perspective Transformation ◽  
Relative Orientation ◽  
Transformation Model ◽  
Reference Image ◽  
General Transformation ◽  
Image Mosaicking ◽  
Uav Images ◽  
Relative Transformation

This paper considers fast and robust mosaicking of UAV images under a circumstance that each UAV images have very narrow overlaps in-between. Image transformation for image mosaicking consists of two estimations: relative transformations and global transformations. For estimating relative transformations between adjacent images, projective transformation is widely considered. For estimating global transformations, panoramic constraint is widely used. While perspective transformation is a general transformation model in 2D-2D transformation, this may not be optimal with weak stereo geometry such as images with narrow overlaps. While panoramic constraint works for reliable conversion of global transformation for panoramic image generation, this constraint is not applicable to UAV images in linear motions. For these reasons, a robust approach is investigated to generate a high quality mosaicked image from narrowly overlapped UAV images. For relative transformations, several transformation models were considered to ensure robust estimation of relative transformation relationship. Among them were perspective transformation, affine transformation, coplanar relative orientation, and relative orientation with reduced adjustment parameters. Performance evaluation for each transformation model was carried out. The experiment results showed that affine transformation and adjusted coplanar relative orientation were superior to others in terms of stability and accuracy. For global transformation, we set initial approximation by converting each relative transformation to a common transformation with respect to a reference image. In future work, we will investigate constrained relative orientation for enhancing geometric accuracy of image mosaicking and bundle adjustments of each relative transformation model for optimal global transformation.

scholarly journals Improved Piecewise Linear Transformation for Precise Warping of Very-High-Resolution Remote Sensing Images

2019 ◽  
Vol 11 (19) ◽  
pp. 2235 ◽  
Author(s):  
Han ◽  
Kim ◽  
Yeom
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Linear Transformation ◽  
Affine Transformation ◽  
Piecewise Linear ◽  
Transformation Model ◽  
Reference Image ◽  
Remote Sensing Images ◽  
Non Linear ◽  
Very High

A large number of evenly distributed conjugate points (CPs) in entirely overlapping regions of the images are required to achieve successful co-registration between very-high-resolution (VHR) remote sensing images. The CPs are then used to construct a non-linear transformation model that locally warps a sensed image to a reference image’s coordinates. Piecewise linear (PL) transformation is largely exploited for warping VHR images because of its superior performance as compared to the other methods. The PL transformation constructs triangular regions on a sensed image from the CPs by applying the Delaunay algorithm, after which the corresponding triangular regions in a reference image are constructed using the same CPs on the image. Each corresponding region in the sensed image is then locally warped to the regions of the reference image through an affine transformation estimated from the CPs on the triangle vertices. The warping performance of the PL transformation shows reliable results, particularly in regions inside the triangles, i.e., within the convex hulls. However, the regions outside the triangles, which are warped when the extrapolated boundary planes are extended using CPs located close to the regions, incur severe geometric distortion. In this study, we propose an effective approach that focuses on the improvement of the warping performance of the PL transformation over the external area of the triangles. Accordingly, the proposed improved piecewise linear (IPL) transformation uses additional pseudo-CPs intentionally extracted from positions on the boundary of the sensed image. The corresponding pseudo-CPs on the reference image are determined by estimating the affine transformation from CPs located close to the pseudo-CPs. The latter are simultaneously used with the former to construct the triangular regions, which are enlarged accordingly. Experiments on both simulated and real datasets, constructed from Worldview-3 and Kompsat-3A satellite images, were conducted to validate the effectiveness of the proposed IPL transformation. That transformation was shown to outperform the existing linear/non-linear transformation models such as an affine, third and fourth polynomials, local weighted mean, and PL. Moreover, we demonstrated that the IPL transformation improved the warping performance over the PL transformation outside the triangular regions by increasing the correlation coefficient values from 0.259 to 0.304, 0.603 to 0.657, and 0.180 to 0.338 in the first, second, and third real datasets, respectively.

scholarly journals DEVELOPMENT OF A ROBUST IMAGE MOSAICKING METHOD FOR SMALL UNMANNED AERIAL VEHICLE

2017 ◽  
Vol XLII-2/W6 ◽  
pp. 183-188 ◽  
Author(s):  
J. Kim ◽  
T. Kim
Keyword(s):  
Extreme Environment ◽  
Area Ratio ◽  
Transformation Model ◽  
Image Transformation ◽  
Image Mosaicking ◽  
Image Blending ◽  
Area Minimization ◽  
Imaging Characteristics ◽  
Preceding Step ◽  
Uav Images

In this paper, a tie-point based image mosaicking method considering imaging characteristics of small UAVs is proposed. Small UAVs can be characterized to have unstable flight trajectory and lower flight height. The proposed method considers the imaging characteristics in image transformation estimation and image blending process. For image transformation estimation, an optimal transformation model is variably applied by using tie-point area ratio. The optimal tie-point area ratio was about 0.3. Mosaicking error was largely decreased by using this tie-point area ratio. For image blending, a composite area minimization is introduced as a preceding step of image resampling. Composite areas of individual images were minimized by analyzing image overlaps between adjacent images. The proposed method was evaluated over flat area and urban area with highly overlapping multi-strip and inconsistently overlapping strip. Experiment results showed that the proposed method can reliably generate mosaics not only from UAV images acquired in good environment but also from extreme environment.

scholarly journals IMAGE NETWORK GENERATION OF UNCALIBRATED UAV IMAGES WITH LOW-COST GPS DATA

2016 ◽  
Vol XLI-B3 ◽  
pp. 31-37 ◽  
Author(s):  
Shan Huang ◽  
Zuxun Zhang ◽  
Jianan He ◽  
Tao Ke
Keyword(s):  
Coordinate System ◽  
Low Cost ◽  
Reference Image ◽  
Model Parameters ◽  
Camera Model ◽  
Orientation Data ◽  
Network Generation ◽  
Free Network ◽  
Uav Images ◽  
Image Network

The use of unmanned air vehicle (UAV) images acquired by a non-metric digital camera to establish an image network is difficult in cases without accurate camera model parameters. Although an image network can be generated by continuously calculating camera model parameters during data processing as an incremental structure from motion (SfM) methods, the process is time consuming. In this study, low-cost global position system (GPS) information is employed in image network generation to decrease computational expenses. Each image is considered as reference, and its neighbor images are determined based on GPS coordinates during processing. The reference image and its neighbor images constitute an image group, which is used to generate a free network through image matching and relative orientation. Data are then transformed from the free network coordinate system of each group into the GPS coordinate system by using the GPS coordinates of each image. After the exterior elements of each image are determined in the GPS coordinate system, the initial image network is established. Finally, self-calibration bundle adjustment constrained by GPS coordinates is conducted to refine the image network. The proposed method is validated on three fields. Results confirm that the method can achieve good image network when accurate camera model parameters are unavailable.

scholarly journals AUTOMATIC COREGISTRATION FOR MULTIVIEW SAR IMAGES IN URBAN AREAS

2017 ◽  
Vol XLII-2/W7 ◽  
pp. 651-654 ◽  
Author(s):  
Y. Xiang ◽  
W. Kang ◽  
F. Wang ◽  
H. You
Keyword(s):  
High Resolution ◽  
Urban Areas ◽  
Structural Information ◽  
Transformation Model ◽  
Reference Image ◽  
Building Detection ◽  
Phase Congruency ◽  
Sar Images ◽  
Local Variance ◽  
Complex Structural

Due to the high resolution property and the side-looking mechanism of SAR sensors, complex buildings structures make the registration of SAR images in urban areas becomes very hard. In order to solve the problem, an automatic and robust coregistration approach for multiview high resolution SAR images is proposed in the paper, which consists of three main modules. First, both the reference image and the sensed image are segmented into two parts, urban areas and nonurban areas. Urban areas caused by double or multiple scattering in a SAR image have a tendency to show higher local mean and local variance values compared with general homogeneous regions due to the complex structural information. Based on this criterion, building areas are extracted. After obtaining the target regions, L-shape structures are detected using the SAR phase congruency model and Hough transform. The double bounce scatterings formed by wall and ground are shown as strong L- or T-shapes, which are usually taken as the most reliable indicator for building detection. According to the assumption that buildings are rectangular and flat models, planimetric buildings are delineated using the L-shapes, then the reconstructed target areas are obtained. For the orignal areas and the reconstructed target areas, the SAR-SIFT matching algorithm is implemented. Finally, correct corresponding points are extracted by the fast sample consensus (FSC) and the transformation model is also derived. The experimental results on a pair of multiview TerraSAR images with 1-m resolution show that the proposed approach gives a robust and precise registration performance, compared with the orignal SAR-SIFT method.

Cylindrical affine transformation model for image registration

2010 ◽  
Author(s):  
Christine Tanner ◽  
Timothy Carter ◽  
David Hawkes ◽  
Gàbor Székely
Keyword(s):  
Image Registration ◽  
Affine Transformation ◽  
Transformation Model

scholarly journals A Novel Information Fusion Method for Vision Perception and Location of Intelligent Industrial Robots

2019 ◽  
Vol 25 (5) ◽  
pp. 4-10 ◽  
Author(s):  
Shoufeng Jin ◽  
Qiangqiang Lin ◽  
Jian Yang ◽  
Yu Bie ◽  
Mingrui Tian ◽  
...  
Keyword(s):  
Affine Transformation ◽  
Hamming Distance ◽  
Industrial Robot ◽  
Hessian Matrix ◽  
Recognition Rate ◽  
Industrial Robots ◽  
Transformation Model ◽  
Feature Points ◽  
Multi Scale ◽  
The Relationship

An improved SURF (Speeded-Up Robust Feature） algorithm is proposed to deal with the time-consuming and low precision of positioning of industrial robot. Hessian matrix determinant is used to extract feature points from the target image and a multi-scale spatial pyramid is constructed. The location and scale value of feature points are determined by neighbourhood non-maximum suppression method. The direction of feature points is defined as directional feature descriptors by the binary robust independent elementary feature (BRIEF). The progressive sample consensus (PROSAC) is used to carry out second precise matching and remove mismatching points based on the Hamming distance. Then, an affine transformation model is established to describe the relationship between the template and target images. Centroid coordinates of the target can be obtained based on the affine transformation. Comparative tests were carried out to demonstrate that the proposed method can effectively improve the recognition rate and positioning accuracy of the industrial robots. The average time consuming is less than 0.2 s, the matching accuracy is 96 %, and the positioning error of the robot is less than 1.5 mm. Therefore, the proposed method has practical application importance.

scholarly journals APPLICATION OF GEOMETRICAL TRANSFORMATIONS FOR THE ATACHMENT OF IMAGES TO GEOGRAPHICAL MAPS

2019 ◽  
Vol 31 (6) ◽  
pp. 1917-1922
Author(s):  
Teodora Petrova
Keyword(s):  
Coordinate System ◽  
Affine Transformation ◽  
Projective Transformation ◽  
Geometric Transformation ◽  
Reference Image ◽  
Earth Surface ◽  
Sar Images ◽  
Geographical Maps ◽  
The One

The development of surveillance instruments allows obtaining images of the one and same object or area of earth surface that have been taken at different times and with different sensors. The analysis of such images requires the use of geometric transformation to align them. In the paper the use of affine transformation for SAR images aligning and the use of projective transformation to register an image to the coordinate system of a map is studied. Aligning of images to maps allows using a map of the area as a reference image instead an orthoimage of this area.

scholarly journals Automatic Seam-Line Detection in UAV Remote Sensing Image Mosaicking by Use of Graph Cuts

2018 ◽  
Vol 7 (9) ◽  
pp. 361 ◽  
Author(s):  
Ming Li ◽  
Deren Li ◽  
Bingxuan Guo ◽  
Lin Li ◽  
Teng Wu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Energy Function ◽  
Graph Cuts ◽  
Remote Sensing Image ◽  
Existing Problems ◽  
Image Mosaicking ◽  
Target Energy ◽  
Aerial Vehicle ◽  
Uav Images ◽  
Uav Image

Image mosaicking is one of the key technologies in data processing in the field of computer vision and digital photogrammetry. For the existing problems of seam, pixel aliasing, and ghosting in mosaic images, this paper proposes and implements an optimal seam-line search method based on graph cuts for unmanned aerial vehicle (UAV) remote sensing image mosaicking. This paper first uses a mature and accurate image matching method to register the pre-mosaicked UAV images, and then it marks the source of each pixel in the overlapped area of adjacent images and calculates the energy value contributed by the marker by using the target energy function of graph cuts constructed in this paper. Finally, the optimal seam-line can be obtained by solving the minimum value of target energy function based on graph cuts. The experimental results show that our method can realize seamless UAV image mosaicking, and the image mosaic area transitions naturally.

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