Number Of Spectral Channels Research Articles

Multiplexing and passive demodulation of intrinsic low-finesse Fabry-Perot interferometers using free-running DFB diode lasers

This paper presents a novel and simple technique for passive quadrature demodulation of multiplexed low-finesse fiber-optic Fabry-Perot interferometers. The approach proposed here uses two quadrature channels with very close wavelengths generated by two thermostabilized DFB diode lasers operating in continuous-wave mode. A coherent light correlation technique was used to demultiplex the wavelength channels and signals from interferometers at different positions along the fiber. The proposed technique does not require optical filters for spectral channel demultiplexing, making it suitable for demodulating relatively long interferometers with any free spectral range. An in-line array of four identical 13.1 cm long interferometers was used to demonstrate the method's performance. Experimental results demonstrate a phase resolution of 1.38 phase degrees, corresponding to a temperature resolution of 3 × 10−3 °C. Numerical simulations indicate that the main factor limiting the length of the sensing interferometer, and thus the resolution of the system, is the wavelength variation of the temperature-stabilized, independently operating lasers. The number of spectral channels can be easily extended to realize three or four-wavelength demodulation schemes.

Deep learning-enhanced snapshot hyperspectral confocal microscopy imaging system

Laser-scanning confocal hyperspectral microscopy is a powerful technique to identify the different sample constituents and their spatial distribution in three-dimensional (3D). However, it suffers from low imaging speed because of the mechanical scanning methods. To overcome this challenge, we propose a snapshot hyperspectral confocal microscopy imaging system (SHCMS). It combined coded illumination microscopy based on a digital micromirror device (DMD) with a snapshot hyperspectral confocal neural network (SHCNet) to realize single-shot confocal hyperspectral imaging. With SHCMS, high-contrast 160-bands confocal hyperspectral images of potato tuber autofluorescence can be collected by only single-shot, which is almost 5 times improvement in the number of spectral channels than previously reported methods. Moreover, our approach can efficiently record hyperspectral volumetric imaging due to the optical sectioning capability. This fast high-resolution hyperspectral imaging method may pave the way for real-time highly multiplexed biological imaging.

Meta-learning to address diverse Earth observation problems across resolutions

Earth scientists study a variety of problems with remote sensing data, but they most often consider them in isolation from each other, which limits information flows across disciplines. In this work, we present METEOR, a meta-learning methodology for Earth observation problems across different resolutions. METEOR is an adaptive deep meta-learning model with several modifications that allow it to ingest images with a variable number of spectral channels and to predict a varying number of classes per downstream task. It uses knowledge mined from land cover information worldwide to adapt to new unseen target problems with few training examples. METEOR outperforms competing self-supervised approaches on five downstream tasks, showing its relevance to addressing novel and impactful geospatial problems with only a handful of labels.

Optimal Number of Spectral Channels for Gaussian Filter Sets in Multispectral Color Imaging

Optimal Number of Spectral Channels for Gaussian Filter Sets in Multispectral Color Imaging

On‐Chip Near‐Infrared Spectral Sensing with Minimal Plasmon‐Modulated Channels

AbstractSpectroscopy is widely used in biomedicine, agriculture, remote sensing, industrial process monitoring, etc. The emerging on‐chip integration techniques further extend these applications to on‐site and portable operating modes. However, realizing high‐applicability and low‐cost spectroscopy chips is still a major challenge particularly in infrared due to the requirements on a large number of spectral channels. Here, a compact and low‐cost near‐infrared spectral sensing platform is demonstrated, releasing the burden of scaling up the spectral channel and simultaneously keeping a high accuracy in chemical classification. The monolithically integrated spectral sensor consists of a small amount of plasmon‐modulated narrowband photodetection units and operates with on‐chip photoelectric responses via a statistical machine learning method. Both plasmonic resonances with low spectral correlation and advanced regression algorithms contribute to high sensing accuracy in the case of limited hardware resources. Chemical concentration quantification, plastic sorting, and scanning spectral imaging are realized using such a sensor with up to twelve spectral channels. This concept avoids the uncertainty of various spectral reconstruction algorithms, high cost, and processing complexity and therefore provides promising strategies for miniaturized spectral sensing platforms.

Parallel multispectral ghost imaging data acquisition with supercontinuum

A wavelength multiplexing with spectral supercontinuum source in computational ghost imaging is implemented and investigated. In our proof-of-concept experiment, we used two diffraction gratings and one spatial light modulator to form up to 10 independent spectral channels. Registration of data by a spectrometer in such a scheme makes it possible to reduce the time of data acquisition by a factor of ten. It was established that an increase the number of spectral channels from 1 to 10 leads to the increase of the contrast-to-noise ratio by the factor of 2.8. This makes the proposed technique attractive for high-speed demanding applications such as communications and remote sensing.

Iris

We propose a novel Visible Light Positioning (VLP) method, called Iris, that leverages light spectral information (LSI) to localize individuals in a completely passive manner. This means that the user does not need to carry any device, and the existing lighting infrastructure remains unchanged. Our method uses a background subtraction approach to accurately detect changes in ambient LSI caused by human movement. Furthermore, we design a Convolutional Neural Network (CNN) capable of learning and predicting user locations from the LSI change data. To validate our approach, we implemented a prototype of Iris using a commercial-off-the-shelf light spectral sensor and conducted experiments in two typical real-world indoor environments: a 25 m2 one-bedroom apartment and a 13.3m × 8.4m office space. Our results demonstrate that Iris performs effectively in both artificial lighting at night and in highly dynamic natural lighting conditions during the day. Moreover, Iris outperforms the state-of-the-art passive VLP techniques significantly in terms of localization accuracy and the required density of light sensors. To reduce the overhead associated with multi-channel spectral sensing, we develop and validate an algorithm that can minimize the required number of spectral channels for a given environment. Finally, we propose a conditional Generative Adversarial Network (cGAN) that can artificially generate LSI and reduce data collection effort by 50% without sacrificing localization accuracy.

A Method for Determining the Nitrogen Content of Wheat Leaves Using Multi-Source Spectral Data and a Convolution Neural Network

Accurate estimation of wheat leaf nitrogen concentration (LNC) is critical for characterizing ecosystem and plant physiological processes; it can further guide fertilization and other field management operations, and promote the sustainable development of agriculture. In this study, a wheat LNC test method based on multi-source spectral data and a convolutional neural network is proposed. First, interpolation reconstruction was performed on the wheat spectra data collected by different spectral instruments to ensure that the number of spectral channels and spectral range were consistent, and multi-source spectral data were constructed using interpolated, reconstructed imaging spectral data and non-imaging spectral data. Afterwards, the convolutional neural network DshNet and machine learning methods (PLSR, SVR, and RFR) were compared under various scenarios (non-imaging spectral data, imaging spectral data, and multi-source spectral data). Finally, the competitive adaptive reweighted sampling (CARS) and successive projections algorithm (SPA) were used to optimize the LNC detection model. The results show that the model based on DshNet has the highest test accuracy. The CARS method is more suitable for DshNet model optimization than SPA. In the modeling scenario with non-imaging spectral, imaging spectral, and multi-source spectral, the optimized R2 is 0.86, 0.82, and 0.82, and the RMSE is 0.29, 0.31, and 0.31, respectively. The LNC visualization results show that DshNet modeling using multi-source spectral data is conducive to the visualization expansion of non-imaging spectral data. Therefore, the method presented in this paper provides new considerations for spectral data from different sources and is helpful for related research on the chemometric task of multi-source spectral data.

Novel method to remotely measure air temperature distribution for indoor environments with heat sources using thermal infrared spectroradiometer

Air temperature is an important physical indicator of a built space. A novel method for remotely measuring the air temperature distribution using a thermal infrared spectroradiometer is evaluated, and an experiment using the maximum a posteriori method, which uses a priori information, is conducted with a heat source. The a priori information is the probabilistic information that the observer knows about the air temperature prior to the observation. Assuming an indoor scale (10 m), the air temperature distribution is estimated (i.e., retrieved) by discretizing the space into four layers. Accordingly, our method reduces the error in the air temperature estimation compared to the a priori information. When the layer of the heat source is known, the air temperature estimation is sensitive to the air temperature of the heated layer, even when the heat source is away from the sensor (2nd to 4th layers). The root-mean-square error (RMSE) of the heated layer decreased from 10 K (a priori) to 4.8 K. Although the RMSE of the temperature distribution was approximately the same regardless of the number of spectral channels used in the retrieval, the RMSE of the heated layer was smaller when ten spectral channels were used. In the case of a target space and spectrometer, as in this study, ten spectral channels were sufficient. These findings indicate the possibility of remotely measuring the air temperature distribution in a space with a local heat source (e.g., data centers), which is a relatively difficult case for remote sensing methods.

Extraction of chlorophyll concentration maps from AOTF hyperspectral imagery

Remote mapping of chlorophyll concentration in leaves is highly important for various biological and agricultural applications. Multiple spectral indices calculated from reflectance at specific wavelengths have been introduced for chlorophyll content quantification. Depending on the crop, environmental factors and task, indices differ. To map them and define the most accurate index, a single multi-spectral imaging system with a limited number of spectral channels is insufficient. When the best chlorophyll index for a particular task is unknown, hyperspectral imager able to collect images at any wavelengths and map multiple indices is in need. Due to precise, fast and arbitrary spectral tuning, acousto-optic imagers provide highly optimized data acquisition and processing. In this study, we demonstrate the feasibility to extract the distribution of chlorophyll content from acousto-optic hyperspectral data cubes. We collected spectral images of soybean leaves of 5 cultivars in the range 450–850 nm, calculated 14 different chlorophyll indices, evaluated absolute value of chlorophyll concentration from each of them via linear regression and compared it with the results of well-established spectrophotometric measurements. We calculated parameters of the chlorophyll content estimation models via linear regression of the experimental data and found that index CIRE demonstrates the highest coefficient of determination 0.993 and the lowest chlorophyll content root-mean-square error 0.66 μg/cm2. Using this index and optimized model, we mapped chlorophyll content distributions in all inspected cultivars. This study exhibits high potential of acousto-optic hyperspectral imagery for mapping spectral indices and choosing the optimal ones with respect to specific crop and environmental conditions.

Remote Nanoscopy with Infrared Elastic Hyperspectral Lidar.

Monitoring insects of different species to understand the factors affecting their diversity and decline is a major challenge. Laser remote sensing and spectroscopy offer promising novel solutions to this. Coherent scattering from thin wing membranes also known as wing interference patterns (WIPs) have recently been demonstrated to be species specific. The colors of WIPs arise due to unique fringy spectra, which can be retrieved over long distances. To demonstrate this, a new concept of infrared (950-1650nm) hyperspectral lidar with 64 spectral bands based on a supercontinuum light source using ray-tracing and 3D printing is developed. A lidar with an unprecedented number of spectral channels, high signal-to-noise ratio, and spatio-temporal resolution enabling detection of free-flying insects and their wingbeats. As proof of principle, coherent scatter from a damselfly wing at 87 m distance without averaging (4 ms recording) is retrieved. The fringed signal properties are used to determine an effective wing membrane thickness of 1412nm with ±4nm precision matching laboratory recordings of the same wing. Similar signals from free flying insects (2ms recording) are later recorded. The accuracy and the method's potential are discussed to discriminate species by capturing coherent features from free-flying insects.

Effects of spectral resolution and smearing on gated word recognition

In a word gating task, listeners are presented with increasing amounts of word-onset information (a series of increasingly longer temporal “gates”), and following each gate, they are asked to indicate what they think the target word is. Listeners with normal hearing (NH) typically recognize a target word well before they “hear” the entire word. Listeners with cochlear implants (CIs), however, need to hear almost the entire target word (require a greater amount of word-onset information) to recognize it. Here, we hypothesized that the poor spectral resolution, due to the limited number of channels and interactions between adjacent channels, may have a negative impact on gated word recognition performance. We manipulated spectral resolution by using: (1) a noise-band vocoder with a variable number of spectral channels; and (2) a vocoder with variable carrier filter slopes to simulate channel interaction. We determined the minimum amount of word-onset information required to recognize spoken words. Initial results suggest that the gated word recognition performance remained roughly unchanged as the amount of spectral degradation applied increased up to some extent, beyond which it deteriorated. With eight channels, which resemble the spectral resolution available to most CI users, even the most focused stimulation yielded poorer results.

Hyperspectral Image Super-Resolution via Deep Spatiospectral Attention Convolutional Neural Networks.

Hyperspectral images (HSIs) are of crucial importance in order to better understand features from a large number of spectral channels. Restricted by its inner imaging mechanism, the spatial resolution is often limited for HSIs. To alleviate this issue, in this work, we propose a simple and efficient architecture of deep convolutional neural networks to fuse a low-resolution HSI (LR-HSI) and a high-resolution multispectral image (HR-MSI), yielding a high-resolution HSI (HR-HSI). The network is designed to preserve both spatial and spectral information thanks to a new architecture based on: 1) the use of the LR-HSI at the HR-MSI's scale to get an output with satisfied spectral preservation and 2) the application of the attention and pixelShuffle modules to extract information, aiming to output high-quality spatial details. Finally, a plain mean squared error loss function is used to measure the performance during the training. Extensive experiments demonstrate that the proposed network architecture achieves the best performance (both qualitatively and quantitatively) compared with recent state-of-the-art HSI super-resolution approaches. Moreover, other significant advantages can be pointed out by the use of the proposed approach, such as a better network generalization ability, a limited computational burden, and the robustness with respect to the number of training samples. Please find the source code and pretrained models from https://liangjiandeng.github.io/Projects_Res/HSRnet_2021tnnls.html.

Multi-wavelength off-axis digital holographic microscopy with broadly tunable low-coherent sources: theory, performance and limitations

Multi-wavelength digital holographic microscopy (MDHM) is widely used in biological and industrial applications because of increased unambiguous height measurement range and the ability to measure concentration from the spectral dependence of phase delay. Acousto-optic tunable filters (AOTFs) provide the simultaneous selection of several bands with tunable central wavelengths to create a multiplexed hologram, but may limit the field of view (FOV) in off-axis holography because of the short coherence length of the filtered light. We analyzed the performance of the AOTF-based off-axis MDHM setup with a diffraction grating or a prism in the reference arm necessary to increase the efficiency of angular multiplexing. This allows varying the number of spectral channels selected simultaneously without setup realignment. Mathematical description relates the spectral bandwidth of the AOTF, tilt of the coherence plane induced by the angular dispersion of a prism or a grating, width of the FOV determined by interference pattern visibility, spatial resolution, and optimal intermediate wavelengths. We theoretically and experimentally demonstrated that the FOV may be expanded by changing the angle of light incidence on the AOTF and that the prism changes the wavelength dependence of the FOV. We validated this technique by single-shot acquisition of the height maps of the transparent test chart at four wavelengths with an error similar to that of four sequentially captured single-wavelength holograms. The results may be helpful for multiple applications of MDHM using spectrally tunable light sources.

Feasibility of Hyperspectral Single Photon Lidar for Robust Autonomous Vehicle Perception.

Autonomous vehicle perception systems typically rely on single-wavelength lidar sensors to obtain three-dimensional information about the road environment. In contrast to cameras, lidars are unaffected by challenging illumination conditions, such as low light during night-time and various bidirectional effects changing the return reflectance. However, as many commercial lidars operate on a monochromatic basis, the ability to distinguish objects based on material spectral properties is limited. In this work, we describe the prototype hardware for a hyperspectral single photon lidar and demonstrate the feasibility of its use in an autonomous-driving-related object classification task. We also introduce a simple statistical model for estimating the reflectance measurement accuracy of single photon sensitive lidar devices. The single photon receiver frame was used to receive 30 12.3 nm spectral channels in the spectral band 1200–1570 nm, with a maximum channel-wise intensity of 32 photons. A varying number of frames were used to accumulate the signal photon count. Multiple objects covering 10 different categories of road environment, such as car, dry asphalt, gravel road, snowy asphalt, wet asphalt, wall, granite, grass, moss, and spruce tree, were included in the experiments. We test the influence of the number of spectral channels and the number of frames on the classification accuracy with random forest classifier and find that the spectral information increases the classification accuracy in the high-photon flux regime from 50% to 94% with 2 channels and 30 channels, respectively. In the low-photon flux regime, the classification accuracy increases from 30% to 38% with 2 channels and 6 channels, respectively. Additionally, we visualize the data with the t-SNE algorithm and show that the photon shot noise in the single photon sensitive hyperspectral data contributes the most to the separability of material specific spectral signatures. The results of this study provide support for the use of hyperspectral single photon lidar data on more advanced object detection and classification methods, and motivates the development of advanced single photon sensitive hyperspectral lidar devices for use in autonomous vehicles and in robotics.

Information reconstruction from noisy channel using ghost imaging method with spectral multiplexing in visible range

The ghost imaging technique allows us to recover information about an object in conditions of noisy transmission channels, commensurate with the intensity of the speckle structures involved in the reconstruction. One of the main disadvantages of this technique is relatively slow reconstruction speed. This limits its applicability for study of dynamic processes or fast-moving objects. In this paper, we propose a modification of the computational ghost imaging technique that allows us to overcome this limitation. It is shown that the spectral multiplexing of the speckle patterns speeds up the image reconstruction. Increase in the number of spectral channels from 4 to 10 leads to the increase of the signal-tonoise ratio by the factor of 6. Simultaneously, under the same conditions and with the same number of measurements classical monochrome ghost imaging does not reconstruct the picture at all. This makes the proposed technique attractive for high-speed demanding applications such as communications and remote sensing.

DWDM signals noise immunity evaluation in a nonlinear fiber-optic path

The total influence of self-phase modulation, phase cross-modulation and four-wave mixing effects and amplified spontaneous emission noise on transmitted pulsed signals noise immunity in a multichannel fiberoptic transmission system with spectral division multiplexing is investigated. The investigation is carried out under the conditions of transmitting system parameters variation: the total optical power level in optical fiber, the transmission speed and the number of spectral channels. The estimate criterion of transmitted pulsed signals noise immunity is Q-factor. The total Q-factor value dependence on the total optical power level in optical fiber for various transmission speed and the number of spectral channels is demonstrated.

Deep Learning-Based Super-Resolution Reconstruction and Algorithm Acceleration of Mars Hyperspectral CRISM Data

In Mars exploration, hyper-spectrometry plays an important role due to its high spectral resolution. However, due to the technical difficulty and the data size, the spatial resolution or the coverage of hyperspectral data is often limited. This limitation can be alleviated by deep learning-based super-resolution (SR) reconstruction. But the spatial size and batch size of the input training data is limited due to the large number of spectral channels. To improve the efficiency of model training and SR reconstruction, a dataset based on CRISM hyperspectral data is created in this paper, and its redundancy is analyzed in both spectral and spatial spital dimensions. Compression algorithms based on data selection and PCA are used to reduce the size of the input training data. A network that can perform spatial SR and spectral enhancement is also proposed to make the network can be trained with the compressed data. With these compression algorithms and network, high-resolution data with 235 bands can be reconstructed from the low-resolution data with only 40 bands. Compared with the network trained on the original low-resolution data with 235 bands, the model training time and the SR reconstruction runtime can be reduced to 30% and 23% with practically no accuracy loss. The effectiveness of compression algorithms based on data selection also indicates that maybe not all the bands need to be transmitted from the Mars probes or be collected. Furthermore, it would, in principle, help improve the efficiency of satellite data transmission and simplify the design of the hyper-spectrometer. Additionally, a method for spatial dimension correlation evaluation is also proposed in this paper. The spatial compression shows that the proposed method can reflect the correlation of spatial texture between patches, and the model can be acceptably trained with only half of the original data.

Analysis of the Efficiency of Hyperspectral Data Classification under Constraints on the Quantization Bit Depth, the Number of Spectral Channels, and Spatial Resolution

Analysis of the Efficiency of Hyperspectral Data Classification under Constraints on the Quantization Bit Depth, the Number of Spectral Channels, and Spatial Resolution

The application of convolutional neural networks for tomographic reconstruction of hyperspectral images

A novel method, utilizing convolutional neural networks (CNNs), is proposed to reconstruct hyperspectral cubes from computed tomography imaging spectrometer (CTIS) images. Current reconstruction algorithms are usually subject to long reconstruction times and mediocre precision in cases of a large number of spectral channels. The constructed CNNs deliver higher precision and shorter reconstruction time than a sparse expectation maximization algorithm. In addition, the network can handle two different types of real-world images at the same time—specifically ColorChecker and carrot spectral images are considered. This work paves the way toward real-time reconstruction of hyperspectral cubes from CTIS images.