David Salido Monzú

Last Name

Salido Monzú

First Name

David

ORCID

0000-0003-4274-6874

Show all metadata

Search Results

Publications 1 - 10 of 19

Supercontinuum-based hyperspectral LiDAR for precision laser scanning
Ray, Pabitro; Salido Monzú, David; Camenzind, Sandro L.; et al. (2023)
Optics Express
Hyperspectral LiDAR enables non-contact mapping of the 3D surface geometry of an object along with its spectral reflectance signature and has proved to be effective for automated point cloud segmentation in various remote sensing applications. The established hyperspectral LiDAR methods offer a range precision of a few mm to a few cm for distances exceeding several meters. We propose a novel approach to hyperspectral LiDAR scanning based on a supercontinuum (SC) coherently broadened from a 780 nm frequency comb. It provides high precision distance measurements along with target reflectance over the 570–970 nm range of the SC output. The distance measurements are carried out by monitoring the differential phase delay of intermode beat notes generated by direct photodetection, while the backscattered light spectrum is acquired using a commercial CCD spectrometer with 0.16 nm resolution across the 400 nm bandwidth of the SC output. We demonstrate a measurement precision below 0.1 mm for a stand-off range up to 50 m on a diffuse target with around 89% reflectance. The measured relative accuracy as compared to a reference interferometer is on the order of 10−5 for distances up to 50 m. Initial results also indicate spectrum-based material classification within a 3D point cloud using a linear support vector machine. The results highlight the potential of this approach for joint high-precision laser scanning and automated material classification.
EDM 3.0 - mehr als nur Distanzen
Wieser, Andreas; Han, Yu; Ray, Pabitro; et al. (2023)
GeoNews
Moderne Lasertechnologie ermöglicht es, EDM-Messungen nicht nur genauer, sondern durch Anreicherung mit Material information auch informativer zu machen. Bis zur kommer ziellen Umsetzung wird es sicher noch eine Zeitlang dauern, aber die aktuelle Forschung zeigt vielversprechende Richtun gen auf, und man darf gespannt sein, wie die Reise weiter geht. Und ganz bestimmt lohnt es sich, von diesem EDM 3.0 zu träumen.
Refractivity corrected distance measurement using the intermode beats derived from a supercontinuum
Ray, Pabitro; Salido Monzú, David; Presl, Robert; et al. (2024)
Optics Express
Simultaneous distance measurements on two or more optical wavelengths enable dispersion-based correction of deviations that result from insufficient knowledge of the refractive index along the signal propagation path. We demonstrate a supercontinuum-based approach for highly accurate distance measurements suitable for such an inline refractivity compensation. The distance is estimated from the phase delay observations on the intermode beats. We use a supercontinuum (SC) coherently broadened from a 780 nm frequency comb and spanning the spectral range of 570-970 nm. Experiments are performed on the 590 and 890 nm wavelength bands filtered from the SC spectrum. Results show distance measurements with standard deviations of around 0.01 mm at 50 m, and a distance-dependent component below 0.2 ppm on the individual spectral bands. Distance residuals compared to a reference interferometer are on the order of 0.1 ppm for displacements up to 50 m. Controlled pressure-induced refractivity variations are created over a length of 15 m, resulting in an optical path length change of 0.4 mm. Using the two-color method, we demonstrate refractivity-corrected distance measurement with a standard deviation of around 0.08 mm for a 60 s averaging window. The current experimental configuration can be easily extended to distance measurements on more than two wavelengths. The results highlight its potential for practical long-distance measurements through inline refractivity compensation.
High-precision intermode beating electro-optic distance measurement for mitigation of atmospheric delays
Ray, Pabitro; Salido Monzú, David; Wieser, Andreas (2023)
Journal of Applied Geodesy
High-precision electro-optic distance measurement (EDM) is essential for deformation monitoring. Although sub-ppm instrumental accuracy is already feasible with state-of-the-art commercial technology, the practically attainable accuracy on distances over more than a few hundred meters is limited by uncertainties in estimating the integral refractive index along the propagation path, which often results in measurement errors of several ppm. This paper presents a new instrumental basis for high-accuracy multispectral EDM using an optical supercontinuum to enable dispersion-based inline refractivity compensation. Initial experiments performed on two spectrally filtered bands of 590 and 890 nm from the supercontinuum show measurement precision better than 0.05 mm over 50 m for an acquisition time of around 3 ms on the individual bands. This represents a comparable performance to our previously reported results on 5 cm by over a range of 3 orders of magnitude longer, which can still be improved by increasing the acquisition time. The preliminary results indicate a relative accuracy of about 0.1 mm at 50 m on each wavelength. Improvement is possible by calibration and by implementing a self-reference scheme that mitigates slow drifts caused by power-to-phase coupling. The results reported herein thus indicate that the presented approach can be further developed for achieving sub-ppm accuracy of refractivity compensated distance measurements on practically useful ranges and under outdoor conditions.
Polarimetric femtosecond-laser LiDAR for multispectral material probing
Han, Yu; Salido Monzú, David; Butt, Jemil Avers; et al. (2022)
Proceedings of SPIE ~ Optics and Photonics for Advanced Dimensional Metrology II
Polarimetric LiDAR combines polarimetry and non-coherent optical ranging techniques to complement the acquisition of geometrical information with material characteristics. In recent decades, polarimetric LiDAR has been widely explored in material probing, target detection, and object identification. These approaches have so far mainly relied on implementations using a single or very few wavelengths. In this work, we propose, develop and evaluate a polarimetric femtosecond-laser LiDAR that enables extracting multispectral polarization signatures on 7 spectral channels of 40 nm spectral bandwidth and 33 spectral channels of 10 nm spectral bandwidth in the visible and near-infrared range. Multispectral polarization signatures of five material specimens (cardboard, foam, plaster, plastic, and wood board) are obtained and used as input features on a linear support vector machine classification algorithm. The results show that extending polarimetric probing to multiple spectral channels improves the classification capabilities with respect to single-wavelength approaches. The combination of different spectral signature dimensions (polarization, reflectance, and distance) that can be derived from LiDAR measurements is also analyzed, with results indicating their capability to support challenging classification tasks.
Simultaneous distance measurement at multiple wavelengths using the intermode beats from a coherent supercontinuum
Salido Monzú, David; Wieser, Andreas (2018)
Journal of Physics: Conference Series
Advances in ultrashort pulse lasers and spectral manipulation enable new approaches to metrological problems in various fields. Dimensional metrology may benefit particularly from this progress, including applications like long distance measurement and 3D laser scanning. Using the intermode beat notes obtained by direct photodetection of a mode-locked femtosecond laser has been demonstrated as a promising alternative to solutions based on actively modulated signals. In this work, we extend the approach to ultra-broadband sources derived from femtosecond lasers, aiming at investigating their potential as a technological basis for multiwavelength distance metrology. We have developed a short-distance experimental set-up for displacement measurement operating simultaneously at two wavelength ranges on both extremes of a 500 nm coherent supercontinuum. The results derived from the phases of the 1 GHz intermode beat notes show that the internal coherence of the source is sufficient to derive distances with an accuracy better than 50 μm. This is a promising first step for the prospective application of this method to develop spectrally-versatile solutions, which is of interest to provide surface material probing capabilities in laser scanning and to increase the accuracy of long distance measurement though dispersion-based refractivity compensation.
Delay-Augmented Spectrometry for Target Classification Using a Frequency-Comb LiDAR
Han, Yu; Salido Monzú, David; Wieser, Andreas (2022)
OSA Technical Digest ~ Conference on Lasers and Electro-Optics
We demonstrate LiDAR-based remote spectrometry of natural targets augmented with delay spectra using an ultra-broadband frequency comb. Material-dependent spectrally-resolved delays with an equivalent sensitivity better than 100 µm complement reflectance signatures for enhanced target classification.
Surface finish classification using depth camera data
Frangez, Valens; Salido Monzú, David; Wieser, Andreas (2021)
Automation in Construction
We propose a novel approach for surface finish classification of digitally fabricated structures using an industrial depth camera. Data collected at different viewpoints are jointly processed to derive the spatial distribution of features describing the reflectance, which is in turn related to the surface finish. The features can be used to classify the surfaces according to their finish e.g., for assessing the homogeneity or conformance. We apply the method to four sprayed plaster specimens of similar visual appearance but different roughness. Using nearest neighbor classification we achieve an accuracy of 97% for the plaster samples. The approach is a contribution towards real-time quality inspection in digital fabrication.
Classification of material and surface roughness using polarimetric multispectral LiDAR
Han, Yu; Salido Monzú, David; Wieser, Andreas (2023)
Proceedings of SPIE ~ Multimodal Sensing and Artificial Intelligence: Technologies and Applications III
Multispectral LiDAR is an emerging active remote sensing technique that combines distance and spectroscopy measurements on light reflected from the surface at the respective measurement point. It is known that the reflectance spectrum can be used for material classification. However, the spectrum also depends on other surface parameters, particularly surface roughness. Herein, we propose an extension of multispectral to polarimetric multispectral LiDAR and introduce polarized and unpolarized reflectance spectra as additional features for classifying materials and roughness. We demonstrate the feasibility and the benefit using a bench-top prototype instrument which allows acquiring standard, polarized and unpolarized reflectance spectra, in addition to distance, in 33 spectral channels with 10 nm bandwidth between 580 and 900 nm. We analyze and interpret the raw spectra obtained from measurements on test specimens consisting of five different materials (PE, PVC, PP, sandstone, limestone) with two different levels of surface roughness. Using a linear support vector machine (SVM) we demonstrate the potential of the different features for independent material and roughness classification. The results indicate that the unpolarized reflectance spectrum increases the material classification accuracy by 50% as compared to a standard spectrum, and that the polarized spectrum actually allows classifying roughness. We interpret the results as a strong indication that multispectral polarimetric LiDAR enables deriving practically relevant additional information on surfaces with high spatial resolution through remote sensing. \end{abstract}
A feature selection method for multimodal multispectral LiDAR sensing
Han, Yu; Salido Monzú, David; Avers Butt, Jemil; et al. (2024)
ISPRS Journal of Photogrammetry and Remote Sensing
Optical remote sensing techniques can indicate the properties of objects by observing different modalities (physical quantities) of the backscattered light at different optical wavelengths. Established examples are reflectance, fluorescence, Raman, or depolarization spectroscopy. LiDAR sensing, on the other hand, allows acquiring the geometry of objects by measuring the propagation delay of optical probing signals. Multimodal multispectral (MM) LiDAR combines these capabilities and extends conventional monochromatic LiDAR in both spectral and modal dimensions within a single instrument, thus enriching point cloud data with non-geometric information. The potentially high dimension of MM LiDAR data, however, poses significant challenges for instrumental design, data acquisition, and data processing. MM LiDAR data are structured as several or all modalities are available in each of the spectral channels. The above challenges can thus be mitigated by feature selection (FS), if the structure of the features is taken into account, i.e., if entire spectral channels or entire modalities are selected or omitted. Herein, we focus on the feature selection method for MM LiDAR and propose a multiclass group feature selection algorithm (MGSVM FS) consisting of a structural sparsity-based embedded feature selection method with an all-in-one support vector machine (SVM). It tackles jointly the challenges arising from the high dimension of the MM data and the need for a multiclass classification task while exploiting the structure of the MM data. In addition, we introduce a complete workflow for evaluating the feature selection and for decision-making. We apply the framework to selecting an optimum spectral and modal configuration for remote material classification using an experimental MM LiDAR system that provides reflectance, distance, and degree of linear polarization in 28 spectral channels of 10 nm width. For the experimental investigation, we use MM LiDAR data obtained in a controlled lab environment from thirty specimens of four material classes relevant for construction. Using all three modalities, we find a configuration with only 3 spectral channels that achieves a classification mean-F1 score of 100% within this small dataset. Similar classification performance can also be achieved with only two modalities when using more spectral channels. MGSVM FS improves the classification mean-F1 score by up to 25% as compared to random selection and outperforms two other commonly used filter and embedded feature selection methods, in this application example. The proposed group feature selection algorithm and decision-making are useful for MM LiDAR, providing a link between instrumental design, data acquisition, and data processing. However, they are also transferable to other application fields related to multiclass classification, regression, and knowledge discovery, with features structured in groups. The collected MM feature dataset, the MGSVM FS algorithm, and the evaluation pipeline are accessible online.

Publications 1 - 10 of 19

David Salido Monzú

Last Name

First Name

ORCID

Organisational unit

Filters

Search Results