A comparison of ground-based hyperspectral imaging and red-edge multispectral imaging for façade material classification

Zahiri, Zohreh; Laefer, Debra F.; Kurz, Tobias H.; Buckley, Simon J.; Gowen, Aoife

doi:10.1016/j.autcon.2022.104164

Cited by 14 publications

(3 citation statements)

References 49 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Hyperspectral imaging is capable of detecting signatures across different wavelengths of light, while multispectral can peer within wavelengths. There are now a wide variety of applications of both to the study of streetscapes in general terms, as sites for the presence of environmental features that can be detected in different wavelengths [102][103][104][105][106]. In other examples, the sensing is targeted specifically at pedestrians on streets.…”

Section: Sensorsmentioning

confidence: 99%

Smart and Sentient Retail High Streets

Torrens

2022

Smart Cities

View full text Add to dashboard Cite

Here, we examine the extension of smart retailing from the indoor confines of stores, outward to high streets. We explore how several technologies at the union of retail intelligence and smart city monitoring could coalesce into retail high streets that are both smart and sentient. We examine the new vantages that smart and sentient retail high streets provide on the customer journey, and how they could transform retailers’ sway over customer experience with new reach to the public spaces around shops. In doing so, we pursue a three-way consideration of these issues, examining the technology that underpins smart retailing, new advances in artificial intelligence and machine learning that beget a level of street-side sentience, and opportunities for retailers to map the knowledge that those technologies provide to individual customer journeys in outdoor settings. Our exploration of these issues takes form as a review of the literature and the introduction of our own research to prototype smart and sentient retail systems for high streets. The topic of enhancing retailers’ acuity on high streets has significant currency, as many high street stores have recently been struggling to sustain custom. However, the production and application of smart and sentient technologies at hyper-local resolution of the streetscape conjures some sobering considerations about shoppers’ and pedestrians’ rights to privacy in public.

show abstract

Section: Sensorsmentioning

confidence: 99%

Smart and Sentient Retail High Streets

Torrens

2022

Smart Cities

View full text Add to dashboard Cite

show abstract

“…The detection accuracy of multispectral cameras is generally low due to their limited number of filters. Typically, these cameras can only provide qualitative analysis of samples [21,22]. In spectral detection technology research, researchers often use either single-directional measurement or integrating sphere measurement to obtain reflection spectrum information [23][24][25].…”

Section: Introductionmentioning

confidence: 99%

Research on Quality Detection of Jujube (Ziziphus jujuba Mill.) Fruit Based on UAV Multi-Spectrum

Ma,

Wang,

Luo

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

To enhance the accuracy of multispectral detection using unmanned aerial vehicles (UAVs), multispectral data of jujube fruit with different soluble solids content (SSC) and moisture content (MC) were obtained under different relative azimuth angles. Prediction models for SSC and MC of jujube fruit were established using partial least squares regression (PLSR) and support vector machines (SVM), respectively. The findings revealed that the MC of jujube fruit had the best prediction effect when the relative azimuth angle was 90°, while the SSC of the jujube fruit had the best prediction effect at an azimuth angle of 180°. Then, the spectral reflectance data corresponding to the eight relative azimuth angles were used as input variables to establish a jujube fruit quality detection model. The results showed that the prediction model for MC and SSC, established using the angle fusion method, had higher detection accuracy compared to the prediction model established at a single angle. This research provides a technical reference for improving the accuracy of outdoor jujube fruit quality detection using spectral technology.

show abstract

“…Nanoscale infrared (nano-IR) is an emerging technique that combines IR spectroscopy with scanning probe microscope to simultaneously acquire the topographic and vibrational chemical information on the sample in a label-free manner. , Nano-IR encompasses nanoscale AFM-IR, nano-FTIR, and other nanoscale IR imaging techniques, which have been widely applied in chemical analysis, − life science, − food safety, − and material science, , as it can readily provide high molecular specificity with nanometer resolution. Yet, the imaging speed of nano-IR is extremely slow due to the point-by-point traversal acquisition of the hyperspectral three-dimensional (3D) data cube.…”

mentioning

confidence: 99%

Fast Nano-IR Hyperspectral Imaging Empowered by Large-Dataset-Free Miniaturized Spatial–Spectral Network

Gao,

Zheng,

Meng

et al. 2024

Anal. Chem.

View full text Add to dashboard Cite

The emerging field of nanoscale infrared (nano-IR) offers label-free molecular contrast, yet its imaging speed is limited by point-by-point traverse acquisition of a three-dimensional (3D) data cube. Here, we develop a spatial–spectral network (SS-Net), a miniaturized deep-learning model, together with compressive sampling to accelerate the nano-IR imaging. The compressive sampling is performed in both the spatial and spectral domains to accelerate the imaging process. The SS-Net is trained to learn the mapping from small nano-IR image patches to the corresponding spectra. With this elaborated mapping strategy, the training can be finished quickly within several minutes using the subsampled data, eliminating the need for a large-labeled dataset of common deep learning methods. We also designed an efficient loss function, which incorporates the image and spectral similarity to enhance the training. We first validate the SS-Net on an open stimulated Raman-scattering dataset; the results exhibit the potential of 10-fold imaging speed improvement with state-of-the-art performance. We then demonstrate the versatility of this approach on atomic force microscopy infrared (AFM-IR) microscopy with 7-fold imaging speed improvement, even on nanoscale Fourier transform infrared (nano-FTIR) microscopy with up to 261.6 folds faster imaging speed. We further showcase the generalization of this method on AFM-force volume-based multiparametric nanoimaging. This method establishes a paradigm for rapid nano-IR imaging, opening new possibilities for cutting-edge research in materials, photonics, and beyond.

show abstract

A comparison of ground-based hyperspectral imaging and red-edge multispectral imaging for façade material classification

Cited by 14 publications

References 49 publications

Smart and Sentient Retail High Streets

Smart and Sentient Retail High Streets

Research on Quality Detection of Jujube (Ziziphus jujuba Mill.) Fruit Based on UAV Multi-Spectrum

Fast Nano-IR Hyperspectral Imaging Empowered by Large-Dataset-Free Miniaturized Spatial–Spectral Network

Contact Info

Product

Resources

About