Multispectral Light Research Articles

A multichannel LED-based lighting approach to improve color discrimination for low vision people

The population of low vision people increases continuously with the acceleration of aging society. As reported by WHO, most of this population is over the age of 50 years and 81% were not concerned by any visual problem before. A visual deficiency can dramatically affect the quality of life and challenge the preservation of a safe independent existence. This study presents a LED-based lighting approach to assist people facing an age-related visual impairment. The research procedure is based on psychophysical experiments consisting in the ordering of standard color samples. Volunteers wearing low vision simulation goggles performed such an ordering under different illumination conditions produced by a 24-channel multispectral lighting system. A filtering technique using color rendering indices coupled with color measurements allowed to objectively determine the lighting conditions providing the best scores in terms of color discrimination. Experimental results were used to combine 3 channels to produce white light inducing a stronger color perception in a low vision context than white LEDs nowadays available for general lighting. Even if further studies will be required, these first results give hope for the design of smart lighting devices that adapt to the visual needs of the visually impaired.

Forest Burn Severity Mapping Using Multispectral Unmanned Aerial Vehicle Images and Light Detection and Ranging (LiDAR) Data: Comparison of Maximum Likelihood, Spectral Angle Mapper, and U-Net Classifiers

Forest Burn Severity Mapping Using Multispectral Unmanned Aerial Vehicle Images and Light Detection and Ranging (LiDAR) Data: Comparison of Maximum Likelihood, Spectral Angle Mapper, and U-Net Classifiers

(Invited) Engineering Color, Pattern, and Texture: From Nature to Materials

Biologically inspired optical materials have garnered attention for development of next-generation coatings, color-changing garments, and interactive/adaptive displays. While biological systems employ tightly coupled chemical, mechanical, and/or electrical controls to achieve changes in color, pattern, and texture with relatively low input energy, engineered systems typically provide less comprehensive function and have significant power requirements due to the limitations of off-the-shelf materials available for manufacturing. The stark differences in performance between bio-inspired, engineered materials and their sources of inspiration necessitate a re-evaluation of the approaches used to build systems designed to emulate biology. In this talk, I will highlight how adaptive display systems in nature have inspired emerging technologies like wearable multispectral light sensors and discuss important considerations for the design and development of future optical and photonic material systems

Classification of Aggregates Using Multispectral Two-Dimensional Angular Light Scattering Simulations.

Airborne particulate matter plays an important role in climate change and health impacts, and is generally irregularly shaped and/or forms agglomerates. These particles may be characterized through their light scattering signals. Two-dimensional angular scattering from such particles produce a speckle pattern that is influenced by their morphology (shape and material composition). In what follows, we revisit morphological descriptors obtained from computationally generated light scattering patterns from aggregates of spherical particles. These descriptors are used as inputs to a multivariate statistical algorithm and then classified via supervised machine learning algorithms. The classification results show improved accuracy over previous efforts and demonstrate the utility of the proposed morphological descriptors.

UAS Hyperspatial LiDAR Data Performance in Delineation and Classification across a Gradient of Wetland Types

Wetlands play a critical role in maintaining stable and productive ecosystems, and they continue to be at heightened risk from anthropogenic and natural degradation, especially along the rapidly developing Atlantic Coastal Plain of North America. As such, strategies to develop up-to-date and high-resolution wetland inventories and classifications remain highly relevant in the context of accelerating sea-level rise and coastal changes. Historically, satellite and airborne remote sensing data along with traditional field-based methods have been used for wetland delineation, yet, more recently, the advent of Uncrewed Aerial Systems (UAS) platforms and sensors is opening new avenues of performing rapid and accurate wetland classifications. To test the relative advantages and limitations of UAS technologies for wetland mapping and classification, we developed wetland classification models using UAS-collected multispectral and UAS-collected light detection and ranging (LiDAR) data relative to airborne-derived LiDAR models of wetland types ranging from palustrine to estuarine. The models were parameterized through a pixel-based random forest algorithm to evaluate model performance systematically and establish variable importance for a suite of variables including topographic, hydrologic, and vegetation-based indices. Based on our experimental results, the average overall classification accuracy and kappa coefficients for the UAS LiDAR-derived models are 75.29% and 0.74, respectively, compared to 79.80% and 0.75 for the airborne LiDAR-derived models, with significant differences in the spatial representation of final wetland classes. The resulting classification maps for the UAS models capture more precise wetland delineations than those of airborne models when trained with ground reference data collected at the same time as the UAS flights. The similar accuracy between the airborne and UAS models suggest that the UAS LiDAR is comparable to the airborne LiDAR. However, given poor revisit time of the airborne surveys and the high spatial resolution and precision of the UAS data, UAS-collected LiDAR provides excellent complementary data to statewide airborne missions or for specific applications that require hyperspatial data. For more structurally complex wetland types (such as the palustrine scrub shrub), UAS hyperspatial LiDAR data performs better and is much more advantageous to use in delineation and classification models. The results of this study contribute towards enhancing wetland delineation and classification models using data collected from multiple UAS platforms.

Enhanced Vascular Features in Porcine Gastrointestinal Endoscopy Using Multispectral Imaging.

Endoscopic investigation is a predominant stan-dard while assessing the gastrointestinal tract. Even though it has been rigorously used in diagnostics for many decades, a high miss rate has been recorded. Advanced endoscopic imaging still has not found solutions to problems like early cancer detection, polyp generality, disease classification, etc. One of the less explored techniques to study early cancer detection is spectral imaging which deals with the absorption and reflection spectra of various wavelengths of light by different layers of tissue. To study tissues under various illumination, a multi-spectral light source unit that can be used along with an endoscopy system was developed with 10 different LEDs of very narrow bandwidths. Using this light source, a feasibility study was per-formed on an animal in which the upper GI tract of a porcine model was imaged and sample images were taken for processing from five different sections. Some wavelengths showed better contrast enhancements for visualization of vascular structures. Wavelength 420 nm (violet light) showed better contrast and the gradient of the line profile histogram showed the highest intensity change between the blood vessels and the surrounding mucosa. These enhancements showed that spectral imaging can potentially help in studying tissues for early cancer detection and improved visualization of the G I tract using endoscopy.

Object based classification of a riparian environment using ultra-high resolution imagery, hierarchical landcover structures, and image texture

Land cover mapping is an important part of resource management, planning, and economic predictions. Improvements in remote sensing, machine learning, image processing, and object based image analysis (OBIA) has made the process of identifying land cover types increasingly faster and reliable but these advances have not been able to utilize all of the information encompassed within ultra-high (sub-meter) resolution imagery. There have been few known attempts to try and maximize this detailed information in high resolution imagery using advanced textural components. Hierarchical land classes are also rarely used as an attribute within the machine learning step of object-based image analysis. In this study we try to circumnavigate the inherent problems associated with high resolution imagery by combining well researched data transformations that aid the OBIA process with a seldom used texture transformation in Geographic Object Based Image Analyses (GEOBIA/OBIA) known as the Gabor Transform and the hierarchal organization of landscapes. We will observe the difference made in segmentation and classification accuracy of a random forest classifier when we fuse a Gabor transformed image to a Normalized Difference Vegetation Index (NDVI), high resolution multi-spectral imagery (RGB and NIR) and Light Detection and Ranging (LiDAR) derived canopy height model (CHM) within a riparian area in Southeast Iowa, United States. Additionally, we will observe the effects on classification accuracy when adding multi-scale land cover data to objects. Both, the addition of hierarchical information and Gabor textural information, could aid the GEOBIA process in delineating and classifying the same objects that human experts would delineate within this riparian landscape.

Compact multispectral photodetectors based on nanodisk arrays atop optical cavity substrates.

It is challenging for the multi-spectral photodetector to have a compact structure, high spectral resolution, and high detection efficiency. This paper reports on a new approach for compact multi-spectral visible light detecting based on the hexagonal lattice silver nanodisk arrays atop optical cavity substrates. Through numerical calculations and optimizations of experiments, we verified that the narrow band responsivity of the photodetector was caused by coupling the surface plasmonic resonances and cavity mode. The multi-spectral photodetector exhibited that the minimum FWHM and the maximum responsivity of was achieved to be 80 nm and 91.5 mA·W-1, respectively. Besides, we also analyzed the influence of the proposed structure on the energy wastage by numerical comparison. The proposed way for multi-spectral photodetector is promising to be an excellent design for the narrow band spectral detection. The design can also be easily integrated with CMOS devices and applied to other spectral regimes for different applications.

CapViT: Cross-context capsule vision transformers for land cover classification with airborne multispectral LiDAR data

Equipped with multiple channels of laser scanners, multispectral light detection and ranging (MS-LiDAR) devices possess more advanced prospects in earth observation tasks compared with their single-band counterparts. It also opens up a potential-competitive solution to conducting land cover mapping with MS-LiDAR devices. In this paper, we develop a cross-context capsule vision transformer (CapViT) to serve for land cover classification with MS-LiDAR data. Specifically, the CapViT is structurized with three streams of capsule transformer encoders, which are stacked by capsule transformer (CapFormer) blocks, to exploit long-range global feature interactions at different context scales. These cross-context feature semantics are finally effectively fused to supervise accurate land cover type inferences. In addition, the CapFormer block parallels dual-path multi-head self-attention modules functioning to interpret both spatial token correlations and channel feature interdependencies, which favor significantly to the semantic promotion of feature encodings. Consequently, with the semantic-promoted feature encodings to boost the feature representation distinctiveness and quality, the land cover classification accuracy is effectively improved. The CapViT is elaborately testified on two MS-LiDAR datasets. Both quantitative assessments and comparative analyses demonstrate the competitive capability and advanced performance of the CapViT in tackling land cover classification issues.

Single Plant Fertilization Using a Robotic Platform in an Organic Cropping Environment

The growing demand for organically produced vegetables requires the adoption of new cropping systems such as strip-cropping. To counteract the additional labour mixed cropping entails, automation and robotics play a key role. This research focuses on the development of a proof-of-concept platform that combines optical sensors and an actuation system for targeted precision fertilization that encircles selected plants rather than a local field area. Two sensor types are used for the detection of a fertilisation need: a multispectral camera and light detection and ranging (LiDAR) devices in order to acquire information on plant health status and three-dimensional characterisation. Specific algorithms were developed to more accurately detect a change in fertilization need. An analysis of their results yields a prescription map for automatic fertilisation through a robotic arm. The relative location of the platform within the prescription map is essential for the correct application of fertilizers, and is acquired through live comparison of a LiDAR pushbroom with the known 3D world model. The geometry of each single plant is taken into account for the application of the sprayed fertiliser. This resulted in a reliable method for the detection of delayed growth and prototype localization within a changing natural environment without relying on external markers.

High resolution multispectral spatial light modulators based on tunable Fabry-Perot nanocavities

Spatial light modulators (SLMs) are the most relevant technology for dynamic wavefront manipulation. They find diverse applications ranging from novel displays to optical and quantum communications. Among commercial SLMs for phase modulation, Liquid Crystal on Silicon (LCoS) offers the smallest pixel size and, thus, the most precise phase mapping and largest field of view (FOV). Further pixel miniaturization, however, is not possible in these devices due to inter-pixel cross-talks, which follow from the high driving voltages needed to modulate the thick liquid crystal (LC) cells that are necessary for full phase control. Newly introduced metasurface-based SLMs provide means for pixel miniaturization by modulating the phase via resonance tuning. These devices, however, are intrinsically monochromatic, limiting their use in applications requiring multi-wavelength operation. Here, we introduce a novel design allowing small pixel and multi-spectral operation. Based on LC-tunable Fabry-Perot nanocavities engineered to support multiple resonances across the visible range (including red, green and blue wavelengths), our design provides continuous 2π phase modulation with high reflectance at each of the operating wavelengths. Experimentally, we realize a device with 96 pixels (~1 μm pitch) that can be individually addressed by electrical biases. Using it, we first demonstrate multi-spectral programmable beam steering with FOV~18° and absolute efficiencies exceeding 40%. Then, we reprogram the device to achieve multi-spectral lensing with tunable focal distance and efficiencies ~27%. Our design paves the way towards a new class of SLM for future applications in displays, optical computing and beyond.

Temporal light control in complex media through the singular-value decomposition of the time-gated transmission matrix

The complex temporal behavior of an ultrashort pulse of light propagating through a multiple scattering medium can be characterized experimentally through a time-gated transmission matrix. Using a spatial light modulator, we demonstrate here that injecting singular vectors of this matrix allows us to optimally control the energy deposition at any controllable delay time. Our approach provides insights into the fundamental aspects of multispectral light scattering and could find applications in imaging or coherent control.

Effect of the Shadow Pixels on Evapotranspiration Inversion of Vineyard: A High-Resolution UAV-Based and Ground-Based Remote Sensing Measurements

Due to the proliferation of precision agriculture, the obstacle of estimating evapotranspiration (ET) and its components from shadow pixels acquired from remote sensing technology should not be neglected. To accurately detect shaded soil and leaf pixels and quantify the implications of shadow pixels on ET inversion, a two-year field-scale observation was carried out in the growing season for a pinot noir vineyard. Based on high-resolution remote sensing sensors covering visible light, thermal infrared, and multispectral light, the supervised classification was applied to detect shadow pixels. Then, we innovatively combined the normalized difference vegetation index with the three-temperature model to quantify the proportion of plant transpiration (T) and soil evaporation (E) in the vineyard ecosystem. Finally, evaluated with the eddy covariance system, we clarified the implications of the shadow pixels on the ET estimation and the spatiotemporal patterns of ET in a vineyard system by considering where shadow pixels were presented. Results indicated that the shadow detection process significantly improved reliable assessment of ET and its components. (1) The shaded soil pixels misled the land cover classification, with the mean canopy cover ignoring shadows 1.68–1.70 times more often than that of shaded area removal; the estimation accuracy of ET can be improved by 4.59–6.82% after considering the effect of shaded soil pixels; and the accuracy can be improved by 0.28–0.89% after multispectral correction. (2) There was a 2 °C canopy temperature discrepancy between sunlit leaves and shaded leaves, meaning that the estimation accuracy of T can be improved by 1.38–7.16% after considering the effect of shaded canopy pixels. (3) Simultaneously, the characteristics showed that there was heterogeneity of ET in the vineyard spatially and that E and T fluxes accounted for 238.05 and 208.79 W·m−2, respectively; the diurnal variation represented a single-peak curve, with a mean of 0.26 mm/h. Our findings provide a better understanding of the influences of shadow pixels on ET estimation using remote sensing techniques.

DETERMINATION OF OPTIMISED PICK-UP AND DROP-OFF LOCATIONS IN TRANSPORT ROUTING – A COST DISTANCE APPROACH

With the emergence of dynamic passenger transport systems, such as demand-responsive transport (DRT) and ride-sharing without predetermined stop locations as used for static bus routes, accurate routing for these flexible door-to-door transport services is needed. Routing between two addresses requires the assignment of addresses to suitable, so-called snapping points as reference points on the road network. Therefore, many conventional routing machines use perpendicular distance to identify the nearest point on the road network. However, this technique tends to produce inaccurate results if the access to a building is not reachable from the road segment with the shortest perpendicular distance. We provide a novel approach to identify the access to buildings (paths) based on remote sensing data to obtain more reasonable stop locations for passenger transport. Multispectral images, OpenStreetMap data, and light detection and ranging (LiDAR) data were used to perform a cost distance analysis based on vegetation cover, building footprints, and the slope of the terrain to identify such optimised stop locations. We assumed that the access to buildings on the shortest route to the building’s entrance consists of little vegetation cover and minimal slope of the terrain; furthermore, the calculated path should not cross building footprints. Thus, snapping points on the road network can be determined based on the most likely path between a building and the road network. We validated our results based on a predetermined ideal snapping area considering different weightings for the parameters slope, vegetation, and building footprints. The results were compared with a conventional routing machine that uses perpendicular distance. This routing machine shows a validation rate of 81.4%, whereas the validation rate of our presented approach is as high as 90.3%. This new approach provides increased accuracy and better comfort for flexible passenger transport systems.

Pro-Cam SSfM: projector–camera system for structure and spectral reflectance from motion

Image-based reconstruction of an object’s 3D shape having the wavelength-by-wavelength spectral reflectance property enables higher-fidelity object 3D modeling compared with typical RGB-based modeling. In this paper, we propose a novel projector–camera system for practical and low-cost acquisition of a dense object 3D model with the spectral reflectance property. Different from existing spectral 3D data acquisition systems that use a dedicated multispectral camera or light, we use a standard RGB camera and an off-the-shelf projector as active illumination for both the 3D reconstruction and the spectral reflectance estimation. We first propose a calibration-free multi-view structured-light method to reconstruct the 3D points while estimating the intrinsic parameters and the poses of both the camera and the projector, which are alternately moved around the object during our image acquisition procedure. We then exploit the projector for multispectral imaging and estimate the spectral reflectance of each 3D point based on a novel spectral reflectance estimation method considering the geometric relationship between the reconstructed 3D points and the estimated projector positions. Experimental results on both synthetic and real data demonstrate that our system can precisely acquire a dense spectral 3D model using off-the-shelf devices.

Abstract TMP9: Novel Single-cell Isolation From Cerebral Cavernous Angioma Specimens For Transcriptomic Studies

Introduction: Cavernous angiomas (CAs) are common neurovascular lesions predisposing patients to seizures and hemorrhagic stroke. Recent evidence shows non-endothelial cell autonomous effects contribute to CA pathogenesis within the dysfunctional neurovascular units (NVUs), consisting of endothelial cells, pericytes, astrocytes, and microglia. Herein, we developed a single-cell isolation protocol to study the transcriptome of each of these cell populations. Method: CAs and control brain tissue were frozen in optimal cutting temperature compound immediately after surgical resection. Tissue was enzymatically digested with Collagenase Type IV and DNase I. The cell suspension was then filtered, washed, and pelleted. Cell debris and myelin were removed using 25% Percoll. A multispectral LED light was used for 30 minutes to reduce background autofluorescence. Cells were stained with a CD31, CD45, CD13, P2RY12, CD49f, GLAST, CD24 and CD90 antibody cocktail. Size, granularity, and antibody-specific gating were set to sort each cell population using the BD FACSymphony S6 Cell Sorter. After isolation, RNA was extracted for each cell population, and the quality was assessed. Cell-specific genes including VWF (endothelial cells), ACTA2 (pericytes ), AQP4 (astrocytes ) and IBA-1 (microglia ), as well as GAPDH were assessed with real-time qPCR (rt-qPCR) for validation. Results: Endothelial cells (CD31 + , CD13 - , CD45 - , CD49f - , GLAST - ), pericytes (CD13 + , CD31 - , CD45 - , CD49f - , GLAST - ), astrocytes (CD49f + , GLAST + ) and microglia (P2RY12 + , CD45 + , CD31 - , CD13 - , CD49f - , GLAST - ) were individually isolated from both 5 CA lesions and 5 control brain tissue using FACS sorting. RNA quantity ranged from 31 to 83 pg/μl, with RNA Integrity Number ranging from 1 to 8.7. Each cell population within the CA lesion was validated by showing differential expression of cell-specific genes. Conclusion: We have shown, for the first-time, feasibility of individual cell type isolation from frozen, surgically excised CA lesions. The RNA quantity and quality of each cell population was suitable for the establishment of cell-specific transcriptome libraries. These will help clarify individual cell type contributions to CA pathogenesis.

Dual near infrared emission in Ag2Se quantum dots via Pb doping for broadband mini light-emitting diodes.

Dual emission almost covering the entire NIR-II region with the maximum full width at half maximum of 542 nm was achieved by doping small amounts of Pb ions into Ag2Se quantum dots, which to the best of our knowledge has not been realized for continuous tunable dual NIR emission in Ag-based QDs. The fabricated broadband NIR mini light-emitting diodes based on the Pb-doped Ag2Se QDs exhibit potential applications for highly sensitive and multispectral non-invasive imaging and NIR lighting.

Multispectral uniform light source system for acquisition of biomedical information

多光谱成像是无创观察生物组织的有效工具，提高光源的辐照均匀性将更有利于准确获取病理组织的多光谱数据。提出基于积分球的多光谱均匀光源系统，为生物组织的多光谱数据采集提供均匀辐照。基于蒙特卡罗光线追迹法对积分球腔体的取位方式进行选择，并对腔体大小、LED布局、积分球输出槽尺寸进行了优化，以确定结构模型的最佳参数；根据优化参数构造了集成11种窄波段LED的实例进行了光学性能验证，实验结果证明所有波段都能在输出槽的主要部分区域实现2%辐照度不均匀性分布；研究了该系统的宽光谱兼容能力，实验结果表明，光谱在输出槽的最大均方根误差为3.74%，具有良好的宽光谱兼容能力；最后，基于所提出的系统进行了静脉可视化测试，用光纤拓展了系统内窥镜的兼容性，并以此探讨了口腔内部牙齿表面病理情况。该系统的多功能性和均匀性在生物医疗图像采集中具有潜在的应用价值。

Retrieving of Temperature and Soot Volume Fraction of the Laminar Diffusion Flame Based on Multi-Spectral Light Field Imaging

Retrieving of Temperature and Soot Volume Fraction of the Laminar Diffusion Flame Based on Multi-Spectral Light Field Imaging

Underwater Light Characteristics of Turbid Coral Reefs of the Inner Central Great Barrier Reef

Hyper-spectral and multi-spectral light sensors were used to examine the effects of elevated suspended sediment concentration (SSC) on the quantity and quality (spectral changes) of underwater downwelling irradiance in the turbid-zone coral reef communities of the inner, central Great Barrier Reef (GBR). Under elevated SSCs the shorter blue wavelengths were preferentially attenuated which together with attenuation of longer red wavelengths by pure water shifted the peak in the underwater irradiance spectrum ~100 nm to the less photosynthetically useful green-yellow waveband (peaking at ~575 nm). The spectral changes were attributed to mineral and detrital content of the terrestrially-derived coastal sediments as opposed to chromophoric (coloured) dissolved organic matter (CDOM). A simple blue to green (B/G, λ455:555 nm) ratio was shown to be useful in detecting sediment (turbidity) related decreases in underwater light as opposed to those associated with clouds which acted as neutral density filters. From a series of vertical profiles through turbid water, a simple, multiple component empirical optical model was developed that could accurately predict the light reduction and associated spectral changes as a function of SSC and water depth for a turbid-zone coral reef community of the inner GBR. The relationship was used to assess the response of a light sensitive coral,Pocillopora verrucosain a 28-d exposure laboratory-based exposure study to a daily light integral of 1 or 6 mol quanta m2. PAR with either a broad spectrum or a green-yellow shifted spectrum. Light reduction resulted in a loss of the algal symbionts (zooxanthellae) of the corals (bleaching) and significant reduction in growth and lipid content. The 6 mol quanta m2d−1PAR treatment with a green-yellow spectrum also resulted in a reduction in the algal density, Chl a content per cm2, lipids and growth compared to the same PAR daily light integral under a broad spectrum. Turbid zone coral reef communities are naturally light limited and given the frequency of sediment resuspension events that occur, spectral shifts are a common and previously unrecognised circumstance. Dedicated underwater light monitoring programs and further assessment of the spectral shifts by suspended sediments are essential for contextualising and further understanding the risk of enhanced sediment run-off to the inshore turbid water communities.