Editorial to Special Issue “Multispectral Image Acquisition, Processing, and Analysis”

Vozel, Benôıt; Bazi, Yakoub

doi:10.3390/rs11192310

Cited by 1 publication

(2 citation statements)

References 13 publications

(11 reference statements)

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“…The 3D positions of the two circle centers were determined using the intersection from their subpixel coordinates in the photo images. The relation between the reference and the measured distance gives the scale factor s, which was applied to T i,high-res raw , according to Equation (2). The final results of the whole multimodal MVP workflow were a dense 3D mesh and the poses of all captured viewpoints, including the multimodal cameras T i,low-res .…”

Section: Implementation Of the Multimodal Mvp Workflowmentioning

confidence: 99%

“…Multimodal imaging has evolved as an essential advancement in computational image analysis and interpretation [1][2][3][4]. Beyond the classical image acquisition by monochrome or color (RGB) cameras, new and/or more comprehensive information is captured by cameras in uncommon spectral regions, such as infrared [5][6][7] and ultraviolet [8][9][10], cameras sensitive to a certain polarization state [11][12][13] or combination of those [14][15][16].…”

Section: Introduction 1motivationmentioning

confidence: 99%

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Fusion of Multimodal Imaging and 3D Digitization Using Photogrammetry

Ramm,

de Dios Cruz,

Heist

et al. 2024

Sensors

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Multimodal sensors capture and integrate diverse characteristics of a scene to maximize information gain. In optics, this may involve capturing intensity in specific spectra or polarization states to determine factors such as material properties or an individual’s health conditions. Combining multimodal camera data with shape data from 3D sensors is a challenging issue. Multimodal cameras, e.g., hyperspectral cameras, or cameras outside the visible light spectrum, e.g., thermal cameras, lack strongly in terms of resolution and image quality compared with state-of-the-art photo cameras. In this article, a new method is demonstrated to superimpose multimodal image data onto a 3D model created by multi-view photogrammetry. While a high-resolution photo camera captures a set of images from varying view angles to reconstruct a detailed 3D model of the scene, low-resolution multimodal camera(s) simultaneously record the scene. All cameras are pre-calibrated and rigidly mounted on a rig, i.e., their imaging properties and relative positions are known. The method was realized in a laboratory setup consisting of a professional photo camera, a thermal camera, and a 12-channel multispectral camera. In our experiments, an accuracy better than one pixel was achieved for the data fusion using multimodal superimposition. Finally, application examples of multimodal 3D digitization are demonstrated, and further steps to system realization are discussed.

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Section: Implementation Of the Multimodal Mvp Workflowmentioning

confidence: 99%