SGM-based seamline determination for urban orthophoto mosaicking

Pang, Shiyan; Sun, Mingwei; Hu, Xiangyun; Zhang, Zuxun

doi:10.1016/j.isprsjprs.2015.11.007

Cited by 35 publications

(22 citation statements)

References 23 publications

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“…According to [31], let R be a rotation transformation that transforms the image coordinate (x, y) to a new coordinate (u, v). Based on p = Hp, a new projective transformation Q that transforms (u, v) to (x , y ) meets p 1], and Q = [q 1 , q 2 , q 3 ; q 4 , q 5 , q 6 ; q 7 , q 8 , 1].…”

Section: Weighting Strategymentioning

confidence: 99%

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Image stitching by line-guided local warping with global similarity constraint

Xiang

Xia

Bai

et al. 2018

Pattern Recognition

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Low-textured image stitching remains a challenging problem. It is difficult to achieve good alignment and it is easy to break image structures due to insufficient and unreliable point correspondences. Moreover, because of the viewpoint variations between multiple images, the stitched images suffer from projective distortions. To solve these problems, this paper presents a line-guided local warping method with a global similarity constraint for image stitching. Line features which serve well for geometric descriptions and scene constraints, are employed to guide image stitching accurately. On one hand, the line features are integrated into a local warping model through a designed weight function. On the other hand, line features are adopted to impose strong geometric constraints, including line correspondence and line colinearity, to improve the stitching performance through mesh optimization. To mitigate projective distortions, we adopt a global similarity constraint, which is integrated with the projective warps via a designed weight strategy. This constraint causes the final warp to slowly change from a projective to a similarity transformation across the image. Finally, the images undergo a two-stage alignment scheme that provides accurate alignment and reduces projective distortion. We evaluate our method on a series of images and compare it with several other methods. The experimental results demonstrate that the proposed method provides a convincing stitching performance and that it outperforms other state-of-the-art methods.

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Section: Weighting Strategymentioning

confidence: 99%

“…Because images are limited by a camera's narrow field of view (FOV), image stitching combines a group of images with overlapping regions to generate a single, but larger, mosaic with a wider FOV. Image stitching has been widely used in many tasks in photogrammetry [1], remote sensing [2] and computer vision [3,4].…”

Section: Introductionmentioning

confidence: 99%

Image stitching by line-guided local warping with global similarity constraint

Xiang

Xia

Bai

et al. 2018

Pattern Recognition

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“…The traditional and popularly used method is to count the number of obvious objects crossed by the detected seamline [4,5,21]. However, this method has two disadvantages.…”

Section: Quality Assessmentmentioning

confidence: 99%

Optimal Seamline Detection for Orthoimage Mosaicking by Combining Deep Convolutional Neural Network and Graph Cuts

Yao

Liu

et al. 2017

Remote Sensing

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When mosaicking orthoimages, especially in urban areas with various obvious ground objects like buildings, roads, cars or trees, the detection of optimal seamlines is one of the key technologies for creating seamless and pleasant image mosaics. In this paper, we propose a new approach to detect optimal seamlines for orthoimage mosaicking with the use of deep convolutional neural network (CNN) and graph cuts. Deep CNNs have been widely used in many fields of computer vision and photogrammetry in recent years, and graph cuts is one of the most widely used energy optimization frameworks. We first propose a deep CNN for land cover semantic segmentation in overlap regions between two adjacent images. Then, the energy cost of each pixel in the overlap regions is defined based on the classification probabilities of belonging to each of the specified classes. To find the optimal seamlines globally, we fuse the CNN-classified energy costs of all pixels into the graph cuts energy minimization framework. The main advantage of our proposed method is that the pixel similarity energy costs between two images are defined using the classification results of the CNN based semantic segmentation instead of using the image informations of color, gradient or texture as traditional methods do. Another advantage of our proposed method is that the semantic informations are fully used to guide the process of optimal seamline detection, which is more reasonable than only using the hand designed features defined to represent the image differences. Finally, the experimental results on several groups of challenging orthoimages show that the proposed method is capable of finding high-quality seamlines among urban and non-urban orthoimages, and outperforms the state-of-the-art algorithms and the commercial software based on the visual comparison, statistical evaluation and quantitative evaluation based on the structural similarity (SSIM) index.

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“…In addition to using a DSM, disparity is another indirect indicator for building areas on neighbouring images. Pang et al (2016) presented an approach to obtain a disparity map for two adjacent DOMs using a semi-global matching method, and then using a threshold to segment areas beyond buildings to help subsequent seamline optimisation.…”

Section: Introductionmentioning

confidence: 99%

Seamline optimisation for urban aerial ortho‐image mosaicking using graph cuts

Zhang

et al. 2018

The Photogrammetric Record

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Optimal seamline detection is a key step when composing digital orthophotomaps (DOM) of extensive areas from overlapping ortho‐images. To avoid seamlines passing through buildings when mosaicking, seamline detection between adjacent ortho‐images is casted as a graph‐cut problem. To avoid buildings, colour differences and gradient magnitude derived from the ortho‐images, together with approximate object heights derived from a digital surface model (DSM), are combined for constructing the cost term of the graph. To efficiently obtain an approximate height value for each DOM unit, an expanded normalised DSM is proposed. Furthermore, the designed cost term for final composite ortho‐image merging is weighted to prioritise pixels closest to the nadir point. Experiments with strips of aerial images show that the proposed method obtains seamless mosaicking of ortho‐images without manual work.

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SGM-based seamline determination for urban orthophoto mosaicking

Cited by 35 publications

References 23 publications

Image stitching by line-guided local warping with global similarity constraint

Image stitching by line-guided local warping with global similarity constraint

Optimal Seamline Detection for Orthoimage Mosaicking by Combining Deep Convolutional Neural Network and Graph Cuts

Seamline optimisation for urban aerial ortho‐image mosaicking using graph cuts

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