scholarly journals A Robust Method for Automatic Panoramic UAV Image Mosaic

Sensors ◽  
2019 ◽  
Vol 19 (8) ◽  
pp. 1898 ◽  
Author(s):  
Jun Chen ◽  
Quan Xu ◽  
Linbo Luo ◽  
Yongtao Wang ◽  
Shuchun Wang
Keyword(s):  
Remote Sensing ◽  
Feature Matching ◽  
Bundle Adjustment ◽  
Image Mosaic ◽  
Robust Method ◽  
Remote Sensing Images ◽  
Ideal Situation ◽  
New Strategy ◽  
Aerial Vehicle ◽  
Uav Image

This paper introduces a robust method for panoramic unmanned aerial vehicle (UAV) image mosaic. In the traditional automatic panoramic image stitching method (Autostitch), it assumes that the camera rotates about its optical centre and the group of transformations the source images may undergo is a special group of homographies. It is rare to get such ideal data in reality. In particular, remote sensing images obtained by UAV do not satisfy such an ideal situation, where the images may not be on a plane yet and even may suffer from nonrigid changes, leading to poor mosaic results. To overcome the above mentioned challenges, in this paper a nonrigid matching algorithm is introduced to the mosaic system to generate accurate feature matching on remote sensing images. We also propose a new strategy for bundle adjustment to make the mosaic system suitable for the UAV image panoramic mosaic effect. Experimental results show that our method outperforms the traditional method and some of the latest methods in terms of visual effect.

scholarly journals Road Extraction from Unmanned Aerial Vehicle Remote Sensing Images Based on Improved Neural Networks

Sensors ◽  
2019 ◽  
Vol 19 (19) ◽  
pp. 4115 ◽  
Author(s):  
Yuxia Li ◽  
Bo Peng ◽  
Lei He ◽  
Kunlong Fan ◽  
Zhenxu Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Neural Networks ◽  
Unmanned Aerial Vehicle ◽  
Computational Efficiency ◽  
Neural Nets ◽  
Road Extraction ◽  
Remote Sensing Images ◽  
Feature Maps ◽  
Aerial Vehicle

Roads are vital components of infrastructure, the extraction of which has become a topic of significant interest in the field of remote sensing. Because deep learning has been a popular method in image processing and information extraction, researchers have paid more attention to extracting road using neural networks. This article proposes the improvement of neural networks to extract roads from Unmanned Aerial Vehicle (UAV) remote sensing images. D-Linknet was first considered for its high performance; however, the huge scale of the net reduced computational efficiency. With a focus on the low computational efficiency problem of the popular D-LinkNet, this article made some improvements: (1) Replace the initial block with a stem block. (2) Rebuild the entire network based on ResNet units with a new structure, allowing for the construction of an improved neural network D-Linknetplus. (3) Add a 1 × 1 convolution layer before DBlock to reduce the input feature maps, reducing parameters and improving computational efficiency. Add another 1 × 1 convolution layer after DBlock to recover the required number of output channels. Accordingly, another improved neural network B-D-LinknetPlus was built. Comparisons were performed between the neural nets, and the verification were made with the Massachusetts Roads Dataset. The results show improved neural networks are helpful in reducing the network size and developing the precision needed for road extraction.

Remote Sensing Image Mosaic Algorithm

2012 ◽  
Vol 500 ◽  
pp. 716-721
Author(s):  
Yi Ding Wang ◽  
Shuai Qin
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Hot Spot ◽  
Remote Sensing Image ◽  
Processing Algorithm ◽  
Geometric Features ◽  
Image Mosaic ◽  
Post Processing ◽  
Remote Sensing Images ◽  
Image Pixels

In the field of remote sensing, the acquirement of higher resolution of remote sensing images has become a hot spot issue with widely use of high resolution of remote sensing images. This paper focus on the characteristics of high resolution remote sensing images, on the basis of fully considerate of the correlation between geometric features and image pixels, bring forward a fusion of image mosaic processing algorithm. With this algorithm, the surface features can be well preserved after the processing of mosaic the remote sensing images, and the overlapping area can transit naturally, it will be better for the post-processing, analysis and application.

scholarly journals 3D target localization based on multi–unmanned aerial vehicle cooperation

2020 ◽  
pp. 002029402092226
Author(s):  
Cheng Xu ◽  
Chanjuan Yin ◽  
Daqing Huang ◽  
Wei Han ◽  
Dongzhen Wang
Keyword(s):  
Remote Sensing ◽  
Unmanned Aerial Vehicle ◽  
Target Localization ◽  
Weight Matrix ◽  
Target Point ◽  
Observation Data ◽  
Remote Sensing Images ◽  
Localization Method ◽  
Ground Target ◽  
Aerial Vehicle

Ground target three-dimensional positions measured from optical remote-sensing images taken by an unmanned aerial vehicle play an important role in related military and civil applications. The weakness of this system lies in its localization accuracy being unstable and its efficiency being low when using a single unmanned aerial vehicle. In this paper, a novel multi–unmanned aerial vehicle cooperative target localization measurement method is proposed to overcome these issues. In the target localization measurement stage, three or more unmanned aerial vehicles simultaneously observe the same ground target and acquire multiple remote-sensing images. According to the principle of perspective projection, the target point, its image point, and the camera’s optic center are collinear, and nonlinear observation equations are established. These equations are then converted to linear equations using a Taylor expansion. Robust weighted least-squares estimation is used to solve the equations with the objective function of minimizing the weighted square sum of re-projection errors from target points to multiple pairs of images, which can make the best use of the effective information and avoid interference from the observation data. An automatic calculation strategy using a weight matrix is designed, and the weight matrix and target-position coordinate value are updated in each iteration until the iteration stopping condition is satisfied. Compared with the stereo-image-pair cross-target localization method, the multi–unmanned aerial vehicle cooperative target localization method can use more observation information, which results in higher rendezvous accuracy and improved performance. Finally, the effectiveness and robustness of this method is verified by numerical simulation and flight testing. The results show that the proposed method can effectively improve the precision of the target’s localization and demonstrates great potential for providing more accurate target localization in engineering applications.

scholarly journals Efficient SfM for Oblique UAV Images: From Match Pair Selection to Geometrical Verification

2018 ◽  
Vol 10 (8) ◽  
pp. 1246 ◽  
Author(s):  
San Jiang ◽  
Wanshou Jiang
Keyword(s):  
Feature Matching ◽  
Bundle Adjustment ◽  
Imaging Systems ◽  
Match Pair ◽  
Software Packages ◽  
Aerial Vehicle ◽  
Selection For ◽  
Image Pairs ◽  
Uav Images ◽  
Reliable Solution

Accurate orientation is required for the applications of UAV (Unmanned Aerial Vehicle) images. In this study, an integrated Structure from Motion (SfM) solution is proposed, which aims to address three issues to ensure the efficient and reliable orientation of oblique UAV images, including match pair selection for large-volume images with large overlap degree, reliable feature matching of images captured from varying directions, and efficient geometrical verification of initial matches. By using four datasets captured with different oblique imaging systems, the proposed SfM solution is comprehensively compared and analyzed. The results demonstrate that linear computational costs can be achieved in feature extraction and matching; although high decrease ratios occur in image pairs, reliable orientation results are still obtained from both the relative and absolute bundle adjustment (BA) tests when compared with other software packages. For the orientation of oblique UAV images, the proposed method can be an efficient and reliable solution.

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