Spectral Error Research Articles

A test of the ability of current bulk optical models to represent the radiative properties of cirrus cloud across the mid- and far-infrared

Abstract. Measurements of mid- to far-infrared nadir radiances obtained from the UK Facility for Airborne Atmospheric Measurements (FAAM) BAe 146 aircraft during the Cirrus Coupled Cloud-Radiation Experiment (CIRCCREX) are used to assess the performance of various ice cloud bulk optical property models. Through use of a minimization approach, we find that the simulations can reproduce the observed spectra in the mid-infrared to within measurement uncertainty, but they are unable to simultaneously match the observations over the far-infrared frequency range. When both mid- and far-infrared observations are used to minimize residuals, first-order estimates of the spectral flux differences between the best-performing simulations and observations indicate a compensation effect between the mid- and far-infrared such that the absolute broadband difference is < 0.7 W m−2. However, simply matching the spectra using the mid-infrared (far-infrared) observations in isolation leads to substantially larger discrepancies, with absolute differences reaching ∼ 1.8 (3.1) W m−2. These results show that simulations using these microphysical models may give a broadly correct integrated longwave radiative impact but that this masks spectral errors, with implicit consequences for the vertical distribution of atmospheric heating. They also imply that retrievals using these models applied to mid-infrared radiances in isolation will select cirrus optical properties that are inconsistent with far-infrared radiances. As such, the results highlight the potential benefit of more extensive far-infrared observations for the assessment and, where necessary, the improvement of current ice bulk optical models.

Linear Spectral Estimate Refinement for Spectral Reconstruction from RGB

Spectral signal recovery from RGB-images based on modern deep learning techniques demonstrated promising results in recent years and offers a feasible alternative to costly or otherwise more complex spectral imaging devices. The state-of-the-art deep learning is formed by approaches that learn a direct end-toend mapping from RGB to spectral images from given RGB and spectral image pairs. Any prior knowledge, most importantly a known spectral responsivity of the imaging device, is not taken into account by the vast majority of deep learning based methods. Although attempts have been made to include prior knowledge with respect to the camera response functions, it remains unclear how to do so in a robust and constructive way. In this work, we propose a hybrid processing method utilizing a handcrafted linear map to directly obtain a good estimate on the spectral signal. Deep learning is only used for a subsequent signal refinement. In contrast to previous work, our linear estimate on the spectral signal is not subject to any network optimization and relies on explicit knowledge on the camera response. It is finally demonstrated that the proposed hybrid processing strategy reduces spectral reconstruction errors.

Convex Polygon Maximization-Based Hyperspectral Endmember Extraction Algorithm

Pure endmember extraction from the hyperspectral image is a very essential step in target detection, classification and unmixing applications. Using the concept of convex geometry, a novel algorithm for endmember extraction is proposed in this paper. The algorithm uses convex polygon maximization to determine a convex set that gives maximum convex polygon area according to the surveyor’s formula. The parallel implementation of the proposed algorithm is useful in finding more effective distinctive pixels. The robustness of the proposed algorithm in the presence of noise is demonstrated with synthetic data. The simulation results, conducted using real hyperspectral data, indicate that the proposed algorithm reduces the Spectral Angle Error (SAE) and Spectral Information Divergence (SID) error by 2.4–8.8%. The effectiveness of the proposed algorithm in abundance mapping is also validated using Root Mean Square Error (RMSE). The RMSE of the proposed algorithm is also improved by 1.7–7.6%.

Brightness Invariant Deep Spectral Super-Resolution.

Spectral reconstruction from RGB or spectral super-resolution (SSR) offers a cheap alternative to otherwise costly and more complex spectral imaging devices. In recent years, deep learning based methods consistently achieved the best reconstruction quality in terms of spectral error metrics. However, there are important properties that are not maintained by deep neural networks. This work is primarily dedicated to scale invariance, also known as brightness invariance or exposure invariance. When RGB signals only differ in their absolute scale, they should lead to identical spectral reconstructions apart from the scaling factor. Scale invariance is an essential property that signal processing must guarantee for a wide range of practical applications. At the moment, scale invariance can only be achieved by relying on a diverse database during network training that covers all possibly occurring signal intensities. In contrast, we propose and evaluate a fundamental approach for deep learning based SSR that holds the property of scale invariance by design and is independent of the training data. The approach is independent of concrete network architectures and instead focuses on reevaluating what neural networks should actually predict. The key insight is that signal magnitudes are irrelevant for acquiring spectral reconstructions from camera signals and are only useful for a potential signal denoising.

A selective multiple fit method of the point of inflection for the large-area HTFP melting plateau curve

National Institute of Metrology (NIM) has set up a new large-area tungsten carbide–carbon (WC-C) high-temperature fixed-point (HTFP) blackbody system as the direct radiation source for measuring spectral irradiance. The point of inflection (POI) of the HTFP melting plateau curve was important to determine the liquidus point and applied as the reference point in temperature calibration. This paper presented a selective multiple fit method. The maximum discrepancy of different factors for the new method and traditional methods were 0.001 K and 0.633 K which led to 0.0003% and 0.20% spectral irradiance errors at 500 nm. In addition, when used to calculate POIs of WC-C and Re-C small-area HTFPs, the maximum deviation between the new method and the average value of traditional methods were −0.004 K and −0.03 K. This method was verified to be insensitive to the influencing factors and more effective for large-area HTFPs applications.

Some remarks on spectral convergence and stability of Iso-Geometric Analysis

We study convergence and stability of Iso-Geometric Analysis (or B-spline method) under k-refinement for splines of maximum smoothness in the setting of the initial value problem for the heat equation. Spectral error bounds for the semi-discrete approximation are shown for the periodic and Dirichlet problems on the unit cube in Rd. Time stepping restrictions for the stability of the θ-scheme are bounded in the case 1∕2<θ≤1 with the help of the best constants in the inverse estimates for splines. In particular, in the periodic case the time stepping does not become more restrictive as k increases. In addition, we show how the fast Fourier transform can be used for an efficient implementation of the scheme for problems with periodic boundary conditions.

Automatic Detection of Negative Symptoms in Schizophrenia via Acoustically Measured Features Associated With Affective Flattening

Among the characteristic symptoms of schizophrenia, negative symptoms are a category. The existence of negative symptoms results in a diminution of normal behaviors and functions for schizophrenic patients. In this research, we focus on the affective flattening of negative symptoms to explore potential biomarkers to achieve automatic diagnosis. This work proposes an automatic procedure for detecting the negative symptoms of schizophrenic patients based on speech signal processing. In this procedure, three features are initially proposed: the symmetric spectral difference level (SSDL), quantization error and vector angle (QEVA), and standard dynamic volume value (SDVV). The SSDL feature is designed with the aim of emphasizing spectral differences that contribute to the evaluation of emotional richness. The QEVA is proposed to reflect the variations in tone using one cumulative error indicator and one variation evaluation indicator. The SDVV feature aims to represent the modulation of speech intensity, considering the speaking behavior of schizophrenic patients. Experiments evaluating the discriminative capabilities of the three features are conducted using a speech database collected from 56 participants (28 schizophrenic patients and 28 healthy controls). The classifier employed in these experiments is a simple decision tree. Three other binary classifiers [linear discrimination (LD), logistic regression (LR), and a support vector machine (SVM)] are also tested to compare their performances with the decision tree in this work. Based on the decision tree classifier, the discrimination accuracy levels of schizophrenic patients and control subjects using the SDLL, QEVA, and SDVV features are in the range of 80.5%-83%, 65%-73%, and 87%- 91.5%, respectively. When the three features are combined, the best discrimination accuracy of schizophrenic patients and control subjects is 98.2%, with an area under the receiver operations characteristic curve (AUC) value of 98%. Note to Practitioners-This article is motivated by the problems of the diagnosis of negative symptoms in schizophrenia and the timely monitoring of schizophrenic patients. In clinic, there exists a high patient-to-clinician ratio and the diagnostic result depends on the subjective experience of the clinician. It is necessary to develop an automatic and objective procedure for the diagnosis and monitoring of schizophrenic patients. Speech disorders are among the salient characteristics of negative symptoms. In this work, an automatic procedure for detecting the negative symptoms based on speech signal processing is proposed. This automatic procedure is achieved based on three newly presented acoustic features involving the characteristics of schizophrenic speech, which consider the speaking behavior of schizophrenic patients. This automatic procedure and the information of the three acoustic features may serve as an aid to clinicians and could potentially help them in providing better monitoring of schizophrenic patients.

Remote sensing data fusion using fruit fly optimization

The image fusion (pan-sharpening) aims to generate a multispectral image with the maximum of spatial details of the panchromatic image and the spectral characteristics of the multispectral images. The generalized intensity-hue-saturation (GIHS) can produce fused images with high spatial details, but the spectral characteristics of the fused images need improvement. This article introduces an adaptive fusion method based on GIHS using the Fruit Fly Optimization Algorithm (FOA) in two stages. First, we suggest to use it to compute the optimal band weights which reduces the large difference between the intensity component and the panchromatic image. Second, we propose to apply the FOA to compute the modulation parameters that estimate the amount of spatial details to be added to the multispectral images. In this regard, an objective function that combines the coefficient of the correlation (CC) with the spatial coefficient of the correlation (SCC) is suggested. This method is tested on Pleiades, IKONOS, and ALSAT-2A images and we compared it with some existing fusion methods. The CC, the Structural Similarity Index (SSIM), The Root Mean Square Error (RMSE), the Quality with No Reference (QNR), the Relative Global Synthesis Error Metric (ERGAS) and the Relative Average Spectral Error (RASE) are used for quantitative analysis. The best values of CC, RMSE, ERGAS, RASE and QNR on all used datasets are given by the proposed method. The quantitative results and the visual analysis demonstrated that the proposed approach outperforms the four comparison methods in terms of spatial and spectral quality.

The effects of misregistration between hyperspectral and panchromatic images on linear spectral unmixing

ABSTRACT Low probability subpixel target extraction and identification is important for hyperspectral (HS) applications. To better exploit the signatures of targets and extract targets from mixed pixels, we proposed a linear spectral unmixing algorithm combining HS and panchromatic (Pan) images (called SU-Co-Ims). However, misregistration between HS and Pan images is common, which usually has a negative impact on subsequent applications. Our motivation is to attempt to quantify the misregistration error range in which the extracted subpixel target is valid. To determine the maximum acceptable misregistration error (MAME), we focus on analysing the impact of misregistration between images on target extraction, that is, the effects of misregistration between images on linear spectral unmixing. Taking Pan image as reference, HS image and Pan image are intentionally misregistered in the along-track direction. The proposed SU-Co-Ims method is applied to decompose mixed pixels and extract subpixel targets. Spectral angle mapper (SAM) and Euclidean distance (ED) are used to evaluate spectral unmixing error introduced by misregistration between images. Results indicate that spectral unmixing error increases with misregistration error, and the MAME varies from 0.35 to 1.94 pixels for imagers with different spatial resolution. Consequently, accurate image registration remains crucial to unmixing-based subpixel target extraction, but misregistration has a low impact on results when the misregistration error between images is smaller than the MAME.

Image fusion framework considering mixed pixels and its application to pansharpening methods based on multiresolution analysis

The fusion of a high-spatial-resolution (HSR) panchromatic band and several multispectral bands with a relative low spatial resolution has become a research focus with the development of HSR remote sensing technology. Previous studies have demonstrated that fused spectra of mixed pixels (MPs) remain mixed, which considerably contributes to spectral distortions observed in fused images produced by most of the current pansharpening methods. Several works have attempted to reduce spectral distortions of fused spectra of MPs to improve the quality of fused products generated by some fusion methods based on component substitution (CS). An image fusion framework for reducing spectral distortions caused by the incorrect fusion of MPs is proposed for both CS and fusion methods based on multiresolution analysis (MRA). Using the proposed framework based on image segmentation, the fused products of two classic MRA-based pansharpening methods were improved by improving the fusion spectra of MPs. The improved fused images were compared with the original fusion products through a fusion experiment using three datasets recorded by WorldView-2, GeoEye-1, and WorldView-3. Experimental results showed that the improved fused products yielded higher Q2n and quality with no reference values and lower relative average spectral error, dimensionless global relative error of synthesis, and spectral angle mapper values than the corresponding original fusion products. This indicates that the proposed image fusion framework is effective for reducing spectral distortions of fused images generated by the two MRA-based fusion methods.

Spatial misregistration in hyperspectral cameras: lab characterization and impact on data quality in real-world images

Hyperspectral cameras capture images where every pixel contains spectral information of the corresponding small area of the depicted scene. Spatial misregistration—differences in spatial sampling between different spectral channels—is one of the key quality parameters of these cameras, because it may have a large impact on the accuracy of the captured spectra. Spatial misregistration unifies various factors, such as differences in the position of the optical point spread function (PSF) in different spectral channels, differences in PSF size, and differences in PSF shape. Ideally, there should be no difference in spatial sampling across the spectral channels, but in any real camera, all these factors are present to some degree. Our work shows the magnitude of the spectral errors caused by these spatial misregistration factors of different magnitudes and in various combinations when acquiring hyperspectral images of real scenes. The spectral errors are calculated in Virtual Camera software where high-resolution airborne images of real-world scenes and several PSFs of different hyperspectral cameras are used as the input. The misregistration factors are simulated. Two different methods for quantifying spatial misregistration in the lab are also tested and compared using the correlation with the errors in the real-world scenes as the criterion. The results are used to suggest the best camera characterization approach that would adequately predict spatial misregistration errors and allow reliable comparison of different hyperspectral cameras to each other.

Learning-Based Approach for Atmospheric Compensation of VNIR Hyperspectral Data

In this work, we deal with the problem of atmospheric compensation (AC) of hyperspectral data collected in the visible and near-infrared (VNIR) spectral range. We propose the “learning-based” approach which uses artificial intelligence algorithms to directly estimate the spectral reflectance from the observed at-sensor radiance image. It uses a parametric regressor whose parameters are learned by means of a strategy based on synthetic data. Such data are generated taking into account 1) the radiative transfer in the atmosphere; 2) the variability of the surface spectral reflectance; and 3) the effects of signal-dependent random noise and spectral miscalibration errors. According to this general framework, we propose a specific multilinear regressor that starting from the knowledge of the atmospheric visibility compensates the water absorption and provides the spectral reflectance of each pixel of the analyzed image. Furthermore, a specific image-based procedure is presented for visibility estimation. The experiment over simulated data is presented and discussed. The test on simulated data aims at showing the effectiveness of the proposed strategy in a completely controlled environment. Experiments are also carried out on three real hyperspectral images acquired by two hyperspectral sensors. The obtained results confirm the effectiveness of the proposed approach by comparing the retrieved reflectance spectra with in-situ measurements or with those obtained by using a well-known commercial AC software.

Inverse design of broadband metasurface absorber based on convolutional autoencoder network and inverse design network

Electromagnetic (EM) metasurfaces have attracted great attention from both engineers and researchers due to their unique physical responses. With the rapid development of complex metasurfaces, the design and optimization processes have also become extremely time-consuming and computational resource-consuming. Here we proposed a deep learning model (DLM) based on a convolutional autoencoder network and inverse design network, which can help to establish the complex relationships between the geometries of metasurfaces and their EM responses. As a typical example, a metasurface absorber consisting of polymethacrylimide foam/metal ring alternating multilayers is chosen to demonstrate the capability of the DLM. The relative spectral error of the two desired spectra is only 5.80 and 5.49, respectively. Our model shows great predictive power and may be used as an effective tool to accelerate the design and optimization of metasurfaces.

Sequential adaptive estimation for spectral reflectance based on camera responses.

A sequential weighted nonlinear regression technique from digital camera responses is proposed for spectral reflectance estimation. The method consists of two stages taking colorimetric and spectral errors between training set and target set into accounts successively. Based on polynomial expansion model, local optimal training samples are adaptively employed to recover spectral reflectance as accurately as possible. The performance of the method is compared with several existing methods in the cases of simulated camera responses under three kinds of noise levels and practical camera responses under the self as well as cross test conditions. Results show that the proposed method is able to recover spectral reflectance with a higher accuracy than other methods considered.

Analysis of Spectral Degradation and Error Compensation in 5G NR Digital Polar Transmitters

Cellular standards evolve to support increasingly higher bandwidths which results in strict in- and out-of-band requirements such as lower error vector magnitude (EVM) and/or adjacent channel leakage ratio (ACLR). Digital polar transmitters, which are showing the best performance in terms of power consumption, are challenged to fulfill these requirements. This work will show that even in case of ideal analog components and infinite digital resources, there are principle limitations in terms of out-of-band noise for digital polar transmitters. A typical polar modulation will be compared to a theoretical signal with continuous amplitude modulation. The analysis of the resulting error signal suggests a decomposition into two components which can be partially compensated in the amplitude and phase modulation paths. Based on that decomposition a compensation algorithm has been developed and evaluated with a 400 MHz 5G New Radio (NR) signal for Band n257 on Frequency Range 2 with carrier frequency of 26.5 GHz. The compensation results in an out-of-band noise reduction of 44 dB close to the carrier frequency. Furthermore, EVM improvements for NR signals are demonstrated.

5-Hz, 150-TW Ti:sapphire Laser with High Spatiotemporal Quality

150-TW, 23-fs laser pulses with high spatial and temporal qualities have been generated at 5 Hz from a chirped-pulse amplification Ti:sapphire laser. The temporal and the spatial qualities of the laser pulse were optimized and characterized for high availability in the fields of high-field science and relativistic laser-matter interactions. The pulse duration was optimized by maximizing the spectral bandwidth and by minimizing the spectral phase error. A temporal contrast was enhanced using an ultrafast Pockels cell and a double plasma mirror, and wavefront aberrations were corrected using an adaptive optics system. Spatiotemporal couplings were characterized by measuring a frequency-resolved wavefront to estimate their effect on the peak intensity at the focus. Due to the high spatiotemporal quality resulting from this optimization and characterization of the laser beam, the 5-Hz 150-TW Ti:sapphire laser will be the workhorse for a variety of research on relativistic laser-matter interactions.

Super-Mode OFDM With Index Modulation

Orthogonal frequency division multiplexing (OFDM) with index modulation (OFDM-IM) appears as a promising multi-carrier waveform candidate for beyond 5G due to its attractive advantages such as operational flexibility and ease of implementation. However, OFDM-IM may not be a proper choice for 5G services such as enhanced mobile broadband (eMBB) since achieving high data rates is challenging because of its null subcarriers. One solution to enhance the spectral efficiency of OFDM-IM is the employment of multiple distinguishable constellations (modes) by also exploiting its null subcarriers for data transmission. This article proposes a novel IM technique called super-mode OFDM-IM (SuM-OFDM-IM), where mode activation patterns (MAPs) and subcarrier activation patterns (SAPs) are jointly selected and conventional data symbols are repetition coded over multiple subcarriers to achieve a diversity gain. For the proposed scheme, a low-complexity detector is designed, theoretical analyses are performed and a bit error rate (BER) upper bound is derived. The performance of the proposed system is also investigated through real-time experiments using a software-defined radio (SDR) based prototype. We show that SuM-OFDM-IM exhibits promising results in terms of spectral efficiency and error performance; thus, appears as a potential candidate for 5G and beyond communication systems.

Assimilating Observations with Spatially Correlated Errors Using a Serial Ensemble Filter with a Multiscale Approach

AbstractThe serial ensemble square root filter (EnSRF) typically assumes observation errors to be uncorrelated when assimilating the observations one at a time. This assumption causes the filter solution to be suboptimal when the observation errors are spatially correlated. Using the Lorenz-96 model, this study evaluates the suboptimality due to mischaracterization of observation error spatial correlations. Neglecting spatial correlations in observation errors results in mismatches between the specified and true observation error variances in spectral space, which cannot be resolved by inflating the overall observation error variance. As a remedy, a multiscale observation (MSO) method is proposed to decompose the observations into multiple scale components and assimilate each component with separately adjusted spectral error variance. Experimental results using the Lorenz-96 model show that the serial EnSRF, with the help from the MSO method, can produce solutions that approach the solution from the EnSRF with correctly specified observation error correlations as the number of scale components increases. The MSO method is further tested in a two-layer quasigeostrophic (QG) model framework. In this case, the MSO method is combined with the multiscale localization (MSL) method to allow the use of different localization radii when updating the model state at different scales. The combined method (MSOL) improves the serial EnSRF performance when assimilating observations with spatially correlated errors. With adjusted observation error spectral variances and localization radii, the combined MSOL method provides the best solution in terms of analysis accuracy and filter consistency. Prospects and challenges are also discussed for the implementation of the MSO method for more complex models and observing networks.

Study on the Effect of Surface Roughness on the Spectral Unmixing of Mixed Pixels

In the spectrum measurement experiment, the roughness of the object surface is an essential factor that cannot be ignored. In this experiment, a group of mixed pixel samples with different mixing ratios were designed, and these samples were printed on four kinds of papers with different roughness. The spectral characteristics of mixed pixels with different roughness are quantitatively analyzed by using the measured spectral data. The linear spectral mixture model is used for spectral decomposition, and the effect of roughness on the unmixing precision of mixed pixels was studied. The surface roughness will affect the reflectivity of the mixed pixel. Specifically, the higher the roughness is, the higher the reflectivity of the sample is. This phenomenon is more noticeable when the proportion of white endmember (PWE) is large, and as the white area ratio decreases, the reflectance difference gradually decreases. When the surface roughness of the sample is less than 3.339 μm, the spectral decomposition is performed using a linear spectral mixing model in the visible light band. The average error of the unmixing is less than 0.53%, which is lower than the conventional standard spectral measurement error. In other words, when the surface roughness of the sample is controlled within a specific range, the effect of roughness on the unmixing accuracy of the mixed pixels is small, and this effect can be almost ignored. Multiple scattering within the pixels is the key to model selection and unmixing accuracy, when using the ASD FieldSpec3 spectrometer to perform spectral reflectance measurement and linear spectral unmixing experiments. If the surface roughness of the sample to be measured is less than the maximum wavelength of the spectrometer, the experimental results believe that the photon energy is mainly mirror reflection on the surface of the object and diffuse reflection. At this time, it is still a better choice to use a linear spectral mixing model to decompose the mixed pixels.

Super-Resolution Mapping Based on Spatial–Spectral Correlation for Spectral Imagery

Due to the influences of imaging conditions, spectral imagery can be coarse and contain a large number of mixed pixels. These mixed pixels can lead to inaccuracies in the land-cover class (LC) mapping. Super-resolution mapping (SRM) can be used to analyze such mixed pixels and obtain the LC mapping information at the subpixel level. However, traditional SRM methods mostly rely on spatial correlation based on linear distance, which ignores the influences of nonlinear imaging conditions. In addition, spectral unmixing errors affect the accuracy of utilized spectral properties. In order to overcome the influence of linear and nonlinear imaging conditions and utilize more accurate spectral properties, the SRM based on spatial–spectral correlation (SSC) is proposed in this work. Spatial correlation is obtained using the mixed spatial attraction model (MSAM) based on the linear Euclidean distance. Besides, a spectral correlation that utilizes spectral properties based on the nonlinear Kullback–Leibler distance (KLD) is proposed. Spatial and spectral correlations are combined to reduce the influences of linear and nonlinear imaging conditions, which results in an improved mapping result. The utilized spectral properties are extracted directly by spectral imagery, thus avoiding the spectral unmixing errors. Experimental results on the three spectral images show that the proposed SSC yields better mapping results than state-of-the-art methods.