A method for stitching remote sensing images with Delaunay triangle feature constraints

Zeng, Weibo; Deng, Qiuyan; Zhao, Xingyue; Li, Dehua; Min, Xinran

doi:10.1080/10106049.2023.2285356

Cited by 4 publications

(4 citation statements)

References 19 publications

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“…Therefore, they may provide a more natural representation of images related to natural landscapes. Triangular meshes, on the other hand, excel in realistically reconstructing the shapes in images, especially when the images contain curves and surfaces [40]. The use of triangular meshes allows for better adaptation to irregular image regions, enabling more flexible shape approximation and, consequently, a more accurate capture of details in the images.…”

Section: Deformable Meshmentioning

confidence: 99%

Rectification for Stitched Images with Deformable Meshes and Residual Networks

Fan,

Mao,

et al. 2024

Applied Sciences

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Image stitching is an important method for digital image processing, which is often prone to the problem of the irregularity of stitched images after stitching. And the traditional image cropping or complementation methods usually lead to a large number of information loss. Therefore, this paper proposes an image rectification method based on deformable mesh and residual network. The method aims to minimize the information loss at the edges of the spliced image and the information loss inside the image. Specifically, the method can select the most suitable mesh shape for residual network regression according to different images. Its loss function includes global loss and local loss, aiming to minimize the loss of image information within the grid and global target. The method in this paper not only greatly reduces the information loss caused by irregular shapes after image stitching, but also adapts to different images with various rigid structures. Meanwhile, its validation on the DIR-D dataset shows that the method outperforms the state-of-the-art methods in image rectification.

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Section: Deformable Meshmentioning

confidence: 99%

Rectification for Stitched Images with Deformable Meshes and Residual Networks

Fan,

Mao,

et al. 2024

Applied Sciences

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show abstract

“…Therefore, they may provide a more natural representation of images related to natural landscapes. Triangular meshes, on the other hand, excel in realistically reconstructing the shapes in images, especially when the images contain curves and surfaces [46]. The use of triangular meshes allows for better adaptation to irregular image regions, enabling more flexible shape approximation and, consequently, a more accurate capture of details in the images.…”

Section: Deformable Meshmentioning

confidence: 99%

Rectification for Image Stitching with Deformable Meshes and Residual Networks

Fan,

Mao,

Mei

et al. 2024

Preprint

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Image stitching is a crucial aspect of image processing. However, factors like perspective and environment often lead to irregular shapes in stitched images. Cropping or completion methods typically result in substantial loss of information. This paper proposes a method for rectifying irregularly images into rectangles using deformable meshes and residual networks. The method utilizes a convolutional neural network to quantify rigid structures of images. Choosing the most suitable mesh structure based on the extraction results, offering options such as triangular, rectangular, and hexagonal. Subsequently, the irregularly image, predefined mesh structure, and predicted mesh structure are input into a wide residual neural network for regression. The loss function comprises local and global, aimed at minimizing the loss of image information within the mesh and global target. This method not only significantly reduces information loss during rectification but also adapting to different images with various rigid structures. Validation on the DIR-D dataset shows this method outperforms state-of-the-art methods in image rectification.

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“…This is time-consuming and does not allow for emergency response. Strategies based on simultaneous localization and mapping (SLAM) [2][3][4] and inter-frame transformation [5][6][7][8][9][10] offer significant speed advantages but suffer from serious cumulative error problems. Currently, it typically relies on global navigation satellite systems (GNSS) and position orientation system (POS) for rectification and geographic coordinates acquisition [5,6], but this method is less reliable for emergency mapping tasks in extreme environments, such as GNSS denial.…”

Section: Introductionmentioning

confidence: 99%

“…Currently, it typically relies on global navigation satellite systems (GNSS) and position orientation system (POS) for rectification and geographic coordinates acquisition [5,6], but this method is less reliable for emergency mapping tasks in extreme environments, such as GNSS denial. Another approach [7][8][9][10] is to minimize cumulative error through a keyframe selection strategy and multiple optimization strategies to achieve greater robustness. In addition, with the rapid development of deep learning, many researchers have attempted to use end-to-end deep neural networks to learn frame-to-frame transformation relationships to avoid error accumulation [11][12][13].…”

Section: Introductionmentioning

confidence: 99%

SatellStitch: Satellite Imagery-Assisted UAV Image Seamless Stitching for Emergency Response without GCP and GNSS

Wei,

Lan,

et al. 2024

Remote Sensing

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Rapidly stitching unmanned aerial vehicle (UAV) imagery to produce high-resolution fast-stitch maps is key to UAV emergency mapping. However, common problems such as gaps and ghosting in image stitching remain challenging and directly affect the visual interpretation value of the imagery product. Inspired by the data characteristics of high-precision satellite images with rich access and geographic coordinates, a seamless stitching method is proposed for emergency response without the support of ground control points (CGPs) and global navigation satellite systems (GNSS). This method aims to eliminate stitching traces and solve the problem of stitching error accumulation. Firstly, satellite images are introduced to support image alignment and geographic coordinate acquisition simultaneously using matching relationships. Then a dynamic contour point set is constructed to locate the stitching region and adaptively extract the fused region of interest (FROI). Finally, the gradient weight cost map of the FROI image is computed and the Laplacian pyramid fusion rule is improved to achieve seamless production of the fast-stitch image map with geolocation information. Experimental results indicate that the method is well adapted to two representative sets of UAV images. Compared with the Laplacian pyramid fusion algorithm, the peak signal-to-noise ratio (PSNR) of the image stitching results can be improved by 31.73% on average, and the mutual information (MI) can be improved by 19.98% on average. With no reliance on CGPs or GNSS support, fast-stitch image maps are more robust in harsh environments, making them ideal for emergency mapping and security applications.

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A method for stitching remote sensing images with Delaunay triangle feature constraints

Cited by 4 publications

References 19 publications

Rectification for Stitched Images with Deformable Meshes and Residual Networks

Rectification for Stitched Images with Deformable Meshes and Residual Networks

Rectification for Image Stitching with Deformable Meshes and Residual Networks

SatellStitch: Satellite Imagery-Assisted UAV Image Seamless Stitching for Emergency Response without GCP and GNSS

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