Hyperspectral Image Stitching via Optimal Seamline Detection

Peng, Zongyi; Ma, Yong; Mei, Xiaoguang; Huang, Jun; Fan, Fan

doi:10.1109/lgrs.2021.3097394

Cited by 9 publications

(6 citation statements)

References 22 publications

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“…Moving on to the literature on hyperspectral feature matching, in image stitching research Yi et al [ 54 ] and Peng et al [ 55 ] have discussed using SIFT as a feature matcher. Mo et al [ 56 ] and Zhang [ 57 ] discussed using SuperPoint [ 48 ] and SuperGlue [ 49 ] for the feature detection and matching step.…”

Section: Related Workmentioning

confidence: 99%

Enhancing Feature Detection and Matching in Low-Pixel-Resolution Hyperspectral Images Using 3D Convolution-Based Siamese Networks

Perera,

Premachandra,

Kawanaka

2023

Sensors

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Today, hyperspectral imaging plays an integral part in the remote sensing and precision agriculture field. Identifying the matching key points between hyperspectral images is an important step in tasks such as image registration, localization, object recognition, and object tracking. Low-pixel resolution hyperspectral imaging is a recent introduction to the field, bringing benefits such as lower cost and form factor compared to traditional systems. However, the use of limited pixel resolution challenges even state-of-the-art feature detection and matching methods, leading to difficulties in generating robust feature matches for images with repeated textures, low textures, low sharpness, and low contrast. Moreover, the use of narrower optics in these cameras adds to the challenges during the feature-matching stage, particularly for images captured during low-altitude flight missions. In order to enhance the robustness of feature detection and matching in low pixel resolution images, in this study we propose a novel approach utilizing 3D Convolution-based Siamese networks. Compared to state-of-the-art methods, this approach takes advantage of all the spectral information available in hyperspectral imaging in order to filter out incorrect matches and produce a robust set of matches. The proposed method initially generates feature matches through a combination of Phase Stretch Transformation-based edge detection and SIFT features. Subsequently, a 3D Convolution-based Siamese network is utilized to filter out inaccurate matches, producing a highly accurate set of feature matches. Evaluation of the proposed method demonstrates its superiority over state-of-the-art approaches in cases where they fail to produce feature matches. Additionally, it competes effectively with the other evaluated methods when generating feature matches in low-pixel resolution hyperspectral images. This research contributes to the advancement of low pixel resolution hyperspectral imaging techniques, and we believe it can specifically aid in mosaic generation of low pixel resolution hyperspectral images.

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Section: Related Workmentioning

confidence: 99%

Enhancing Feature Detection and Matching in Low-Pixel-Resolution Hyperspectral Images Using 3D Convolution-Based Siamese Networks

Perera,

Premachandra,

Kawanaka

2023

Sensors

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“…To evaluate the ultrasound image stitching quality, we used the following quantitative indices: subjective evaluation criteria (SEC) [17] , peak signal-to-noise ratio (PSNR) [18] , the Structural Similarity Index (SSIM) [19] , and mutual information (MI) [20] .…”

Section: Evaluation Of Image Stitchingmentioning

confidence: 99%

Ultrasonic image stitching method for material defect detection

Song

Chen

2023

International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2023)

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Defect panoramic ultrasound image is helpful for material integrity evaluation in NDT&E. However, the si ngle scan area of ultrasonic transducer is limited, which leading to the limitation of image area. Due to the low resolution and minimal detail in ultrasound images of defect, traditional visual image stitching methods cannot be directly applied to ultrasound defect images. Therefore, an ultrasonic image stitching method for material defect detection is proposed. According to the feature analysis of ultrasound defect images, an improved scale-invariant feature transform algorithm for ultrasound images stitching is developed. The algorithm first identifies image feature points in the same horizontal layer for coarse matching and then uses Euclidean distance histograms to eliminate mismatched pairs for fine matching. Thereafter, the algorithm transfers different images into the same coordinate system by using a transformation matrix obtained from a random sample consensus algorithm with an adaptive threshold. Finally, an improved fade -in/fade-out weighted fusion algorithm is used to stich the images. Acoustic field simulations and actual test experiments have been performed on an aluminum test sample to obtain a panoramic ultrasound defect image by using the proposed method.

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“…2: Extract the feature points by SuperPoint and obtain a putative match set S between two adjacent images set in the reference band. 3: Use kernel convolution to generate a typical motion field using Equations ( 4) and ( 5) 4: Obtain inlier set R * and matching points {x k l,r , x k r,l } by adaptive filtering using Equation (11) 5: for p = 1 to N − 1 do 6:…”

Section: Algorithm 1 Hyperspectral Panoramic Image Stitching Using Ro...mentioning

confidence: 99%

“…Existing hyperspectral image-stitching methods have certain limitations. For example, in [11], UAV-based HSIs were stitched using an optimal seamline detection approach. However, this work limited its application to large-scale and multi-image stitching tasks, which presented a severe challenge in solving accumulation errors, and spectral distortion was noticeable.…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral Panoramic Image Stitching Using Robust Matching and Adaptive Bundle Adjustment

Zhang

Mei

et al. 2022

Remote Sensing

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Remote-sensing developments such as UAVs heighten the need for hyperspectral image stitching techniques that can obtain information on a large area through various parts of the same scene. State-of-the-art approaches often suffer from accumulation errors and high computational costs for large-scale hyperspectral remote-sensing images. In this study, we aim to generate high-precision hyperspectral panoramas with less spatial and spectral distortion. We introduce a new stitching strategy and apply it to hyperspectral images. The stitching framework was built as follows: First, a single band obtained by signal-to-noise ratio estimation was chosen as the reference band. Then, a feature-matching method combining the SuperPoint and LAF algorithms was adopted to strengthen the reliability of feature correspondences. Adaptive bundle adjustment was also designed to eliminate misaligned artifact areas and occasional accumulation errors. Lastly, a spectral correction method using covariance correspondences is proposed to ensure spectral consistency. Extensive feature-matching and image-stitching experiments on several hyperspectral datasets demonstrate the superiority of our approach over the state of the art.

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Hyperspectral Image Stitching via Optimal Seamline Detection

Cited by 9 publications

References 22 publications

Enhancing Feature Detection and Matching in Low-Pixel-Resolution Hyperspectral Images Using 3D Convolution-Based Siamese Networks

Enhancing Feature Detection and Matching in Low-Pixel-Resolution Hyperspectral Images Using 3D Convolution-Based Siamese Networks

Ultrasonic image stitching method for material defect detection

Hyperspectral Panoramic Image Stitching Using Robust Matching and Adaptive Bundle Adjustment

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