Seamless UAV Hyperspectral Image Stitching Using Optimal Seamline Detection via Graph Cuts

Peng, Zongyi; Ma, Yong; Zhang, Yujie; Li, Hao; Fan, Fan; Mei, Xiaoguang

doi:10.1109/tgrs.2023.3275970

Cited by 7 publications

(3 citation statements)

References 66 publications

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“…Another method to eliminate the artifacts is by using a seam line. To achieve satisfactory seam results, the way of finding the optimal seam path needs to be considered [ 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 ]. By defining a loss function and calculating the cost under different seam lines, a unique and optimal seam path can be determined.…”

Section: Introductionmentioning

confidence: 99%

“…But pixel-based methods do not consider the semantic information in the images and their receptive fields are small. Correspondingly, superpixel partition naturally considers the boundary information of different objects, and using superpixels as the nodes can enlarge the receptive fields [ 28 , 29 , 30 ]. Yuan et al [ 28 ] have designed the energy function of the nodes from the perspectives of color difference and texture complexity.…”

Section: Introductionmentioning

confidence: 99%

“…The color difference is processed in the YUV space, while the texture complexity is represented by the Gabor filter. Peng et al [ 29 ] stitched hyperspectral images considering both space and spectral information of HSI. Miao et al [ 30 ] divided the overlapping area into superpixels, which are also divided into foreground and background.…”

Section: Introductionmentioning

confidence: 99%

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Semantic Aware Stitching for Panorama

Jia,

Li,

Zhang

et al. 2024

Sensors

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The most critical aspect of panorama generation is maintaining local semantic consistency. Objects may be projected from different depths in the captured image. When warping the image to a unified canvas, pixels at the semantic boundaries of the different views are significantly misaligned. We propose two lightweight strategies to address this challenge efficiently. First, the original image is segmented as superpixels rather than regular grids to preserve the structure of each cell. We propose effective cost functions to generate the warp matrix for each superpixel. The warp matrix varies progressively for smooth projection, which contributes to a more faithful reconstruction of object structures. Second, to deal with artifacts introduced by stitching, we use a seam line method tailored to superpixels. The algorithm takes into account the feature similarity of neighborhood superpixels, including color difference, structure and entropy. We also consider the semantic information to avoid semantic misalignment. The optimal solution constrained by the cost functions is obtained under a graph model. The resulting stitched images exhibit improved naturalness. Extensive testing on common panorama stitching datasets is performed on the algorithm. Experimental results show that the proposed algorithm effectively mitigates artifacts, preserves the completeness of semantics and produces panoramic images with a subjective quality that is superior to that of alternative methods.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Semantic Aware Stitching for Panorama

Jia,

Li,

Zhang

et al. 2024

Sensors

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Unmanned aerial vehicle image stitching based on multi‐region segmentation

Pan,

Li,

Liu

et al. 2024

IET Image Processing

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Unmanned aerial vehicle (UAV) image stitching is a key technology for aerial remote sensing applications. Most existing image stitching methods based on optimal seamline searching algorithms can eliminate defects such as ghosting and distortion in stitched images, which unfortunately suffer from the problem that the seamline may cross those regions with significant geometric misalignment between different images. Therefore, a novel image stitching method based on multi‐region image segmentation is proposed. The algorithm starts by performing a multi‐scale morphological reconstruction in the overlapping regions between UAV images to obtain superpixel images with precise contours. Then, the fast density peaks clustering based on K‐nearest neighbours is applied to automatically determine the clustering centres and the number of clusters. By constructing a cost function, an energy map is generated in the overlapping regions between UAV images. Finally, the optimal seamline can be determined with a graph‐cut method. Compared to several popular image stitching algorithms in real experiments, the proposed method can essentially prevent the seamline from crossing significant ground objects to ensure the integrity of structural objects while achieving satisfactory accuracy and efficiency during the UAV image stitching process.

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