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Xiuxiao Yuan
School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China

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Letter
Published: 09 December 2020 in Sensors
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Image stitching based on a global alignment model is widely used in computer vision. However, the resulting stitched image may look blurry or ghosted due to parallax. To solve this problem, we propose a parallax-tolerant image stitching method based on nonrigid warping in this paper. Given a group of putative feature correspondences between overlapping images, we first use a semiparametric function fitting, which introduces a motion coherence constraint to remove outliers. Then, the input images are warped according to a nonrigid warp model based on Gaussian radial basis functions. The nonrigid warping is a kind of elastic deformation that is flexible and smooth enough to eliminate moderate parallax errors. This leads to high-precision alignment in the overlapped region. For the nonoverlapping region, we use a rigid similarity model to reduce distortion. Through effective transition, the nonrigid warping of the overlapped region and the rigid warping of the nonoverlapping region can be used jointly. Our method can obtain more accurate local alignment while maintaining the overall shape of the image. Experimental results on several challenging data sets for urban scene show that the proposed approach is better than state-of-the-art approaches in both qualitative and quantitative indicators.

ACS Style

Lixia Deng; Xiuxiao Yuan; Cailong Deng; Jun Chen; Yang Cai. Image Stitching Based on Nonrigid Warping for Urban Scene. Sensors 2020, 20, 7050 .

AMA Style

Lixia Deng, Xiuxiao Yuan, Cailong Deng, Jun Chen, Yang Cai. Image Stitching Based on Nonrigid Warping for Urban Scene. Sensors. 2020; 20 (24):7050.

Chicago/Turabian Style

Lixia Deng; Xiuxiao Yuan; Cailong Deng; Jun Chen; Yang Cai. 2020. "Image Stitching Based on Nonrigid Warping for Urban Scene." Sensors 20, no. 24: 7050.

Journal article
Published: 02 July 2020 in Sensors
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Large radiometric and geometric distortion in multi-source images leads to fewer matching points with high matching blunder ratios, and global geometric relationship models between multi-sensor images are inexplicit. Thus, traditional matching blunder detection methods cannot work effectively. To address this problem, we propose two matching blunder detection methods based on graph theory. The proposed methods can build statistically significant clusters in the case of few matching points with high matching blunder ratios, and use local geometric similarity constraints to detect matching blunders when the global geometric relationship is not explicit. The first method (named the complete graph-based method) uses clusters constructed by matched triangles in complete graphs to encode the local geometric similarity of images, and it can detect matching blunders effectively without considering the global geometric relationship. The second method uses the triangular irregular network (TIN) graph to approximate a complete graph to reduce to computational complexity of the first method. We name this the TIN graph-based method. Experiments show that the two graph-based methods outperform the classical random sample consensus (RANSAC)-based method in recognition rate, false rate, number of remaining matching point pairs, dispersion, positional accuracy in simulated and real data (image pairs from Gaofen1, near infrared ray of Gaofen1, Gaofen2, panchromatic Landsat, Ziyuan3, Jilin1and unmanned aerial vehicle). Notably, in most cases, the mean false rates of RANSAC, the complete graph-based method and the TIN graph-based method in simulated data experiments are 0.50, 0.26 and 0.14, respectively. In addition, the mean positional accuracy (RMSE measured in units of pixels) of the three methods is 2.6, 1.4 and 1.5 in real data experiments, respectively. Furthermore, when matching blunder ratio is no higher than 50%, the computation time of the TIN graph-based method is nearly equal to that of the RANSAC-based method, and roughly 2 to 40 times less than that of the complete graph-based method.

ACS Style

Cailong Deng; Xiuxiao Yuan; Lixia Deng; Jun Chen. Detecting Matching Blunders of Multi-Source Remote Sensing Images via Graph Theory. Sensors 2020, 20, 3712 .

AMA Style

Cailong Deng, Xiuxiao Yuan, Lixia Deng, Jun Chen. Detecting Matching Blunders of Multi-Source Remote Sensing Images via Graph Theory. Sensors. 2020; 20 (13):3712.

Chicago/Turabian Style

Cailong Deng; Xiuxiao Yuan; Lixia Deng; Jun Chen. 2020. "Detecting Matching Blunders of Multi-Source Remote Sensing Images via Graph Theory." Sensors 20, no. 13: 3712.

Journal article
Published: 11 July 2018 in Remote Sensing
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Matching multi-sensor remote sensing images is still a challenging task due to textural changes and non-linear intensity differences. In this paper, a novel matching method is proposed for multi-sensor remote sensing images. To establish feature correspondences, an affinity tensor is used to integrate geometric and radiometric information. The matching process consists of three steps. First, features from an accelerated segment test are extracted from both source and target images, and two complete graphs are constructed with their nodes representing these features. Then, the geometric and radiometric similarities of the feature points are represented by the three-order affinity tensor, and the initial feature correspondences are established by tensor power iteration. Finally, a tensor-based mismatch detection process is conducted to purify the initial matched points. The robustness and capability of the proposed method are tested with a variety of remote sensing images such as Ziyuan-3 backward, Ziyuan-3 nadir, Gaofen-1, Gaofen-2, unmanned aerial vehicle platform, and Jilin-1. The experiments show that the average matching recall is greater than 0.5, which outperforms state-of-the-art multi-sensor image-matching algorithms such as SIFT, SURF, NG-SIFT, OR-SIFT and GOM-SIFT.

ACS Style

Shiyu Chen; Xiuxiao Yuan; Wei Yuan; Jiqiang Niu; Feng Xu; Yong Zhang. Matching Multi-Sensor Remote Sensing Images via an Affinity Tensor. Remote Sensing 2018, 10, 1104 .

AMA Style

Shiyu Chen, Xiuxiao Yuan, Wei Yuan, Jiqiang Niu, Feng Xu, Yong Zhang. Matching Multi-Sensor Remote Sensing Images via an Affinity Tensor. Remote Sensing. 2018; 10 (7):1104.

Chicago/Turabian Style

Shiyu Chen; Xiuxiao Yuan; Wei Yuan; Jiqiang Niu; Feng Xu; Yong Zhang. 2018. "Matching Multi-Sensor Remote Sensing Images via an Affinity Tensor." Remote Sensing 10, no. 7: 1104.

Journal article
Published: 10 August 2017 in Remote Sensing
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Power line inspection ensures the safe operation of a power transmission grid. Using unmanned aerial vehicle (UAV) images of power line corridors is an effective way to carry out these vital inspections. In this paper, we propose an automatic inspection method for power lines using UAV images. This method, known as the power line automatic measurement method based on epipolar constraints (PLAMEC), acquires the spatial position of the power lines. Then, the semi patch matching based on epipolar constraints (SPMEC) dense matching method is applied to automatically extract dense point clouds within the power line corridor. Obstacles can then be automatically detected by calculating the spatial distance between a power line and the point cloud representing the ground. Experimental results show that the PLAMEC automatically measures power lines effectively with a measurement accuracy consistent with that of manual stereo measurements. The height root mean square (RMS) error of the point cloud was 0.233 m, and the RMS error of the power line was 0.205 m. In addition, we verified the detected obstacles in the field and measured the distance between the canopy and power line using a laser range finder. The results show that the difference of these two distances was within ±0.5 m.

ACS Style

Yong Zhang; Xiuxiao Yuan; Wenzhuo Li; Shiyu Chen. Automatic Power Line Inspection Using UAV Images. Remote Sensing 2017, 9, 824 .

AMA Style

Yong Zhang, Xiuxiao Yuan, Wenzhuo Li, Shiyu Chen. Automatic Power Line Inspection Using UAV Images. Remote Sensing. 2017; 9 (8):824.

Chicago/Turabian Style

Yong Zhang; Xiuxiao Yuan; Wenzhuo Li; Shiyu Chen. 2017. "Automatic Power Line Inspection Using UAV Images." Remote Sensing 9, no. 8: 824.

Journal article
Published: 12 January 2017 in ISPRS International Journal of Geo-Information
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When the distance between an obstacle and a power line is less than the discharge distance, a discharge arc can be generated, resulting in the interruption of power supplies. Therefore, regular safety inspections are necessary to ensure the safe operation of power grids. Tall vegetation and buildings are the key factors threatening the safe operation of extra high voltage transmission lines within a power line corridor. Manual or laser intensity direction and ranging (LiDAR) based inspections are time consuming and expensive. To make safety inspections more efficient and flexible, a low-altitude unmanned aerial vehicle (UAV) remote-sensing platform, equipped with an optical digital camera, was used to inspect power line corridors. We propose a semi-patch matching algorithm based on epipolar constraints, using both the correlation coefficient (CC) and the shape of its curve to extract three dimensional (3D) point clouds for a power line corridor. We use a stereo image pair from inter-strip to improve power line measurement accuracy by transforming the power line direction to an approximately perpendicular to epipolar line. The distance between the power lines and the 3D point cloud is taken as a criterion for locating obstacles within the power line corridor automatically. Experimental results show that our proposed method is a reliable, cost effective, and applicable way for practical power line inspection and can locate obstacles within the power line corridor with accuracy better than ±0.5 m.

ACS Style

Yong Zhang; Xiuxiao Yuan; Yi Fang; Shiyu Chen. UAV Low Altitude Photogrammetry for Power Line Inspection. ISPRS International Journal of Geo-Information 2017, 6, 14 .

AMA Style

Yong Zhang, Xiuxiao Yuan, Yi Fang, Shiyu Chen. UAV Low Altitude Photogrammetry for Power Line Inspection. ISPRS International Journal of Geo-Information. 2017; 6 (1):14.

Chicago/Turabian Style

Yong Zhang; Xiuxiao Yuan; Yi Fang; Shiyu Chen. 2017. "UAV Low Altitude Photogrammetry for Power Line Inspection." ISPRS International Journal of Geo-Information 6, no. 1: 14.

Preprint
Published: 05 August 2016
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When the distance between an obstacle and a power line is less than the discharge distance, a discharge arc can be generated, resulting in interruption of power supplies. Therefore, regular safety inspections are necessary to ensure safe operations of power grids. Tall vegetation and buildings are the key factors threatening the safe operation of extra high voltage transmission lines within a power line corridor. Manual or LiDAR based-inspections are time consuming and expensive. To make safety inspections more efficient and flexible, a low-altitude unmanned aerial vehicle remote-sensing platform equipped with optical digital camera was used to inspect power line corridors. We propose a semi-patch matching algorithm based on epipolar constraints using both correlation coefficient and the shape of its curve to extract three dimensional (3D) point clouds for a power line corridor. Virtual photography was used to transform the power line direction from approximately parallel to the epipolar line to approximately perpendicular to epipolar line to improve power line measurement accuracy. The distance between the power lines and the 3D point cloud is taken as a criterion for locating obstacles within the power line corridor automatically. Experimental results show that our proposed method is a reliable, cost effective and applicable way for practical power line inspection, and can locate obstacles within the power line corridor with measurement accuracies better than ±0.5 m.

ACS Style

Yong Zhang; Xiuxiao Yuan; Yi Fang; Shiyu Chen. UAV Low Altitude Photogrammetry for Power Line Inspection. 2016, 1 .

AMA Style

Yong Zhang, Xiuxiao Yuan, Yi Fang, Shiyu Chen. UAV Low Altitude Photogrammetry for Power Line Inspection. . 2016; ():1.

Chicago/Turabian Style

Yong Zhang; Xiuxiao Yuan; Yi Fang; Shiyu Chen. 2016. "UAV Low Altitude Photogrammetry for Power Line Inspection." , no. : 1.

Journal article
Published: 02 April 2016 in GPS Solutions
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We extend the application of real-time kinematic PPP to aerial triangulation using GPS to determine coordinates of the antenna installed on the airplane, using real-time satellite products from IGS and the CNES Analysis Center. In order to verify the performance of real-time kinematic PPP for aerial triangulation, three tests with varying aerial and ground conditions are assessed. Numerical results show that real-time kinematic PPP using IGS real-time products of 5-cm orbit accuracy and 0.1- to 0.3-ns clock precision can provide comparable accuracy for aerial photogrammetric mapping at the scale of 1:1000 as does post-mission kinematic PPP using IGS final products. Millimeter-to-centimeter-level differences and centimeter-to-2-decimeter differences are identified for horizontal and vertical coordinates of ground check points, respectively, in the three tests. The comparison between real-time IGS and CNES products for GPS positioning and aerial triangulation unveils that real-time products with a better clock precision can result in better performance of GPS real-time kinematic PPP as applied to aerial triangulation.

ACS Style

Junbo Shi; Xiuxiao Yuan; Yang Cai; Gaojing Wang. GPS real-time precise point positioning for aerial triangulation. GPS Solutions 2016, 21, 405 -414.

AMA Style

Junbo Shi, Xiuxiao Yuan, Yang Cai, Gaojing Wang. GPS real-time precise point positioning for aerial triangulation. GPS Solutions. 2016; 21 (2):405-414.

Chicago/Turabian Style

Junbo Shi; Xiuxiao Yuan; Yang Cai; Gaojing Wang. 2016. "GPS real-time precise point positioning for aerial triangulation." GPS Solutions 21, no. 2: 405-414.

Journal article
Published: 13 April 2015 in ISPRS Journal of Photogrammetry and Remote Sensing
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Georeferencing image sequences is critical for mobile mapping systems. Traditional methods such as bundle adjustment need adequate and well-distributed ground control points (GCP) when accurate GPS data are not available in complex urban scenes. For applications of large areas, automatic extraction of GCPs by matching vehicle-born image sequences with geo-referenced ortho-images will be a better choice than intensive GCP collection with field surveying. However, such image matching generated GCP’s are highly noisy, especially in complex urban street environments due to shadows, occlusions and moving objects in the ortho images. This study presents a probabilistic solution that integrates matching and localization under one framework. First, a probabilistic and global localization model is formulated based on the Bayes’ rules and Markov chain. Unlike many conventional methods, our model can accommodate non-Gaussian observation. In the next step, a particle filtering method is applied to determine this model under highly noisy GCP’s. Owing to the multiple hypotheses tracking represented by diverse particles, the method can balance the strength of geometric and radiometric constraints, i.e., drifted motion models and noisy GCP’s, and guarantee an approximately optimal trajectory. Carried out tests are with thousands of mobile panoramic images and aerial ortho-images. Comparing with the conventional extended Kalman filtering and a global registration method, the proposed approach can succeed even under more than 80% gross errors in GCP’s and reach a good accuracy equivalent to the traditional bundle adjustment with dense and precise control.

ACS Style

Shunping Ji; Yun Shi; Jie Shan; Xiaowei Shao; Zhongchao Shi; Xiuxiao Yuan; Peng Yang; Wenbin Wu; Huajun Tang; Ryosuke Shibasaki. Particle filtering methods for georeferencing panoramic image sequence in complex urban scenes. ISPRS Journal of Photogrammetry and Remote Sensing 2015, 105, 1 -12.

AMA Style

Shunping Ji, Yun Shi, Jie Shan, Xiaowei Shao, Zhongchao Shi, Xiuxiao Yuan, Peng Yang, Wenbin Wu, Huajun Tang, Ryosuke Shibasaki. Particle filtering methods for georeferencing panoramic image sequence in complex urban scenes. ISPRS Journal of Photogrammetry and Remote Sensing. 2015; 105 ():1-12.

Chicago/Turabian Style

Shunping Ji; Yun Shi; Jie Shan; Xiaowei Shao; Zhongchao Shi; Xiuxiao Yuan; Peng Yang; Wenbin Wu; Huajun Tang; Ryosuke Shibasaki. 2015. "Particle filtering methods for georeferencing panoramic image sequence in complex urban scenes." ISPRS Journal of Photogrammetry and Remote Sensing 105, no. : 1-12.