Spectral Gradient Research Articles

Optimization Techniques to Deeply Mine the Transcriptomic Profile of the Sub-Genomes in Hybrid Fish Lineage.

It has been shown that reciprocal cross allodiploid lineage with sub-genomes derived from the cross of Megalobrama amblycephala (BSB) × Culter alburnus (TC) generates the variations in phenotypes and genotypes, but it is still a challenge to deeply mine biological information in the transcriptomic profile of this lineage owing to its genomic complexity and lack of efficient data mining methods. In this paper, we establish an optimization model by non-negative matrix factorization approach for deeply mining the transcriptomic profile of the sub-genomes in hybrid fish lineage. A new so-called spectral conjugate gradient algorithm is developed to solve a sequence of large-scale subproblems such that the original complicated model can be efficiently solved. It is shown that the proposed method can provide a satisfactory result of taxonomy for the hybrid fish lineage such that their genetic characteristics are revealed, even for the samples with larger detection errors. Particularly, highly expressed shared genes are found for each class of the fish. The hybrid progeny of TC and BSB displays significant hybrid characteristics. The third generation of TC-BSB hybrid progeny ( and ) shows larger trait separation.

Exact spectral-like gradient method for distributed optimization

Since the initial proposal in the late 80s, spectral gradient methods continue to receive significant attention, especially due to their excellent numerical performance on various large scale applications. However, to date, they have not been sufficiently explored in the context of distributed optimization. In this paper, we consider unconstrained distributed optimization problems where n nodes constitute an arbitrary connected network and collaboratively minimize the sum of their local convex cost functions. In this setting, building from existing exact distributed gradient methods, we propose a novel exact distributed gradient method wherein nodes’ step-sizes are designed according to the novel rules akin to those in spectral gradient methods. We refer to the proposed method as Distributed Spectral Gradient method. The method exhibits R-linear convergence under standard assumptions for the nodes’ local costs and safeguarding on the algorithm step-sizes. We illustrate the method’s performance through simulation examples.

KLUM: An Urban VNIR and SWIR Spectral Library Consisting of Building Materials

Knowledge about the existing materials in urban areas has, in recent times, increased in importance. With the use of imaging spectroscopy and hyperspectral remote sensing techniques, it is possible to measure and collect the spectra of urban materials. Most spectral libraries consist of either spectra acquired indoors in a controlled lab environment or of spectra from afar using airborne systems accompanied with in situ measurements. Furthermore, most publicly available spectral libraries have, so far, not focused on facade materials but on roofing materials, roads, and pavements. In this study, we present an urban spectral library consisting of collected in situ material spectra with imaging spectroscopy techniques in the visible and near-infrared (VNIR) and short-wave infrared (SWIR) spectral range, with particular focus on facade materials and material variation. The spectral library consists of building materials, such as facade and roofing materials, in addition to surrounding ground material, but with a focus on facades. This novelty is beneficial to the community as there is a shift to oblique-viewed Unmanned Aerial Vehicle (UAV)-based remote sensing and thus, there is a need for new types of spectral libraries. The post-processing consists partly of an intra-set solar irradiance correction and recalculation of reference spectra caused by signal clipping. Furthermore, the clustering of the acquired spectra was performed and evaluated using spectral measures, including Spectral Angle and a modified Spectral Gradient Angle. To confirm and compare the material classes, we used samples from publicly available spectral libraries. The final material classification scheme is based on a hierarchy with subclasses, which enables a spectral library with a larger material variation and offers the possibility to perform a more refined material analysis. The analysis reveals that the color and the surface structure, texture or coating of a material plays a significantly larger role than what has been presented so far. The samples and their corresponding detailed metadata can be found in the Karlsruhe Library of Urban Materials (KLUM) archive.

Fusion‐based spectral matching method for hyperspectral target detection

Target detection using hyperspectral images is one of the most important applications in the field of both civilian and military. Some spectral similarity metric methods have been used in those applications, i.e. spectral angle mapper and spectral gradient angle and so on. However, those methods only consider the similarity between two spectral in one aspect, and many information hidden in the spectrum is ignored. Then, here, the authors propose a new fusion-based spectral-matching method for hyperspectral target-detection task. The proposed Euclidean-angle matching method combines the magnitude and the shape difference together to determine the similarity between the test and target spectra samples. Authors’ results indicate that the proposed fusion-based method exhibits a better performance over the other spectral-matching methods.

Probing the Response of Tropical Deep Convection to Aerosol Perturbations Using Idealized Cloud-Resolving Simulations with Parameterized Large-Scale Dynamics

Abstract A framework is introduced to investigate the indirect effect of aerosol loading on tropical deep convection using three-dimensional limited-domain idealized cloud-system-resolving model simulations coupled with large-scale dynamics over fixed sea surface temperature. The large-scale circulation is parameterized using the spectral weak temperature gradient (WTG) approximation that utilizes the dominant balance between adiabatic cooling and diabatic heating in the tropics. The aerosol loading effect is examined by varying the number of cloud condensation nuclei (CCN) available to form cloud droplets in the two-moment bulk microphysics scheme over a wide range of environments from 30 to 5000 cm−3. The radiative heating is held at a constant prescribed rate in order to isolate the microphysical effects. Analyses are performed over the period after equilibrium is achieved between convection and the large-scale environment. Mean precipitation is found to decrease modestly and monotonically when the aerosol number concentration increases as convection gets weaker, despite the increase in cloud liquid water in the warm-rain region and ice crystals aloft. This reduction is traced down to the reduction in surface enthalpy fluxes as an energy source to the atmospheric column induced by the coupling of the large-scale motion, though the gross moist stability remains constant. Increasing CCN concentration leads to 1) a cooler free troposphere because of a reduction in the diabatic heating and 2) a warmer boundary layer because of suppressed evaporative cooling. This dipole temperature structure is associated with anomalously descending large-scale vertical motion above the boundary layer and ascending motion at lower levels. Sensitivity tests suggest that changes in convection and mean precipitation are unlikely to be caused by the impact of aerosols on cloud droplets and microphysical properties but rather by accounting for the feedback from convective adjustment with the large-scale dynamics. Furthermore, a simple scaling argument is derived based on the vertically integrated moist static energy budget, which enables estimation of changes in precipitation given known changes in surfaces enthalpy fluxes and the constant gross moist stability. The impact on cloud hydrometeors and microphysical properties is also examined, and it is consistent with the macrophysical picture.

Frequency Domain-Based Features for Hyperspectral Image Classification

Frequency spectrum has been proven to have the potential in hyperspectral image classification and ground object recognition. The characteristics of the frequency spectrum, such as dc component, descent rate, and spectrum oscillation, are different from each other; thus, based on the discrepancy in the frequency spectrum, 14 frequency spectrum features, including frequency spectrum integration area, spectral centroid ( $C_{k}$ and $C_{k-\textrm {log}}$ ), spectral rolloff ( $C_{t}$ ), spectral flux, spectral gradient of peaks, and valley, number of crosspoint, and first three peaks and valleys position, are proposed. To evaluate the performance of the proposed features, two commonly used hyperspectral images were taken as experimental data sets. Then, we employed three frequently used classification methods to perform the experiment based on spectral-only and frequency-spectral features. The results show that the proposed features can distinctly prompt the classification accuracies by combining the original spectral features.

Spectral modified Polak–Ribiére–Polyak projection conjugate gradient method for solving monotone systems of nonlinear equations

In this paper, we present a modification of Polak–Ribie´re–Polyak (PRP) conjugate gradient method for solving system of monotone nonlinear equations which is a combination of spectral conjugate gradient method and the hyperplane projection technique. The method is based on two methods for unconstrained optimization proposed by Wan et al. (2011) and Sun (2015). We obtained a new search direction by the use of a different formula for the conjugate gradient parameter. The search direction satisfies the sufficient descent condition and the global convergence of the method is established under some assumptions. Preliminary numerical comparison with some existing methods shows the efficiency of the proposed method.

Polarization modeling and predictions for Daniel K. Inouye Solar Telescope part 5: impacts of enhanced mirror and dichroic coatings on system polarization calibration

The Daniel K. Inouye Solar Telescope (DKIST) is designed to deliver accurate spectropolarimetric calibrations across a wide wavelength range and large field of view for solar disk, limb, and coronal observations. DKIST instruments deliver spectral resolving powers of up to 300,000 in multiple cameras of multiple instruments sampling nanometer scale bandpasses. We require detailed knowledge of optical coatings on all optics to ensure that we can predict and calibrate the polarization behavior of the system. Optical coatings can be metals protected by many dielectric layers or several-micron-thick dichroics. Strong spectral gradients up to 60 deg retardance per nanometer wavelength and several percent diattenuation per nanometer wavelength are observed in such coatings. Often, optical coatings are not specified with spectral gradient targets for polarimetry in combination with both average- and spectral threshold-type specifications. DKIST has a suite of interchangeable dichroic beam splitters using up to 96 layers. We apply the Berreman formalism in open-source Python scripts to derive coating polarization behavior. We present high spectral resolution examples on dichroics where transmission can drop 10% with associated polarization changes over a 1-nm spectral bandpass in both mirrors and dichroics. We worked with a vendor to design dichroic coatings with relatively benign polarization properties that pass spectral gradient requirements and polarization requirements in addition to reflectivity. We now have the ability to fit multilayer coating designs which allow us to predict system-level polarization properties of mirrors, antireflection coatings, and dichroics at arbitrary incidence angles, high spectral resolving power, and on curved surfaces through optical modeling software packages. Performance predictions for polarization at large astronomical telescopes require significant metrology efforts on individual optical components combined with system-level modeling efforts. We show our custom-built laboratory spectropolarimeter and metrology efforts on protected metal mirrors, antireflection coatings, and dichroic mirror samples.

A LAND COVER CHANGE DETECTION METHOD BASED ON CHANGE DIFFERENCE MAP FUSION

Abstract. Direct radiometric comparison-based change detection methods have been widely used for detecting land cover change areas. However, the traditional methods are usually developed by using a single change index, which may cause omission or commission of land cover change. To address this challenge, we propose a change difference map fusion-based land cover change detection approach. First, operators of change vector analysis (CVA) and spectral gradient difference (SGD) are used for constructing change difference map, respectively. Second, image product weighted fusion method is introduced to construct a comprehensive change difference map. Finally, the expectation maximization algorithm and Bayesian rule with minimum error rate are applied to get the change/unchanged area. Experimental results of Landsat5 TM in 2010 and Landsat8 OLI in 2015 in two test areas are conducted. The experimental results show that accuracy of the proposed method is superior to the traditional change detection methods.

A modified conjugate gradient method for monotone nonlinear equations with convex constraints

In this paper, a modified Hestenes-Stiefel (HS) spectral conjugate gradient (CG) method for monotone nonlinear equations with convex constraints is proposed based on projection technique. The method can be viewed as an extension of a modified HS-CG method for unconstrained optimization proposed by Amini et al. (Optimization Methods and Software, pp: 1-13, 2018). A new search direction is obtained by incorporating the idea of spectral gradient parameter and some modification of the conjugate gradient parameter. The proposed method is derivative-free and requires low memory which makes it suitable for large scale monotone nonlinear equations. Global convergence of the method is established under suitable assumptions. Preliminary numerical comparisons with some existing methods are given to show the efficiency of our proposed method. Furthermore, the proposed method is successfully applied to solve sparse signal reconstruction in compressive sensing.

Integrating cross-sensor high spatial resolution satellite images to detect subtle forest vegetation change in the Purple Mountains, a national scenic spot in Nanjing, China

Accurate information on the location and magnitude of vegetation change in scenic areas can guide the configuration of tourism facilities and the formulation of vegetation protection measures. High spatial resolution remote sensing images can be used to detect subtle vegetation changes. The major objective of this study was to map and quantify forest vegetation changes in a national scenic location, the Purple Mountains of Nanjing, China, using multi-temporal cross-sensor high spatial resolution satellite images to identify the main drivers of the vegetation changes and provide a reference for sustainable management. We used Quickbird images acquired in 2004, IKONOS images acquired in 2009, and WorldView2 images acquired in 2015. Four pixel-based direct change detection methods including the normalized difference vegetation index difference method, multi-index integrated change analysis (MIICA), principal component analysis, and spectral gradient difference analysis were compared in terms of their change detection performances. Subsequently, the best pixel-based detection method in conjunction with object-oriented image analysis was used to extract subtle forest vegetation changes. An accuracy assessment using the stratified random sampling points was conducted to evaluate the performance of the change detection results. The results showed that the MIICA method was the best pixel-based change detection method. And the object-oriented MIICA with an overall accuracy of 0.907 and a kappa coefficient of 0.846 was superior to the pixel-based MIICA. From 2004 to 2009, areas of vegetation gain mainly occurred around the periphery of the study area, while areas of vegetation loss were observed in the interior and along the boundary of the study area due to construction activities, which contributed to 79% of the total area of vegetation loss. During 2009–2015, the greening initiatives around the construction areas increased the forest vegetation coverage, accounting for 84% of the total area of vegetation gain. In spite of this, vegetation loss occurred in the interior of the Purple Mountains due to infrastructure development that caused conversion from vegetation to impervious areas. We recommend that: (1) a local multi-agency team inspect and assess law enforcement regarding natural resource utilization; and (2) strengthen environmental awareness education.

A family of spectral gradient methods for optimization

We propose a family of spectral gradient methods, whose stepsize is determined by a convex combination of the long Barzilai-Borwein (BB) stepsize and the short BB stepsize. Each member of the family is shown to share certain quasi-Newton property in the sense of least squares. The family also includes some other gradient methods as its special cases. We prove that the family of methods is $R$-superlinearly convergent for two-dimensional strictly convex quadratics. Moreover, the family is $R$-linearly convergent in the any-dimensional case. Numerical results of the family with different settings are presented, which demonstrate that the proposed family is promising.

First Spectral Analysis of a Solar Plasma Eruption Using ALMA

Abstract The aim of this study is to demonstrate how the logarithmic millimeter continuum gradient observed using the Atacama Large Millimeter/submillimeter Array (ALMA) may be used to estimate optical thickness in the solar atmosphere. We discuss how using multiwavelength millimeter measurements can refine plasma analysis through knowledge of the absorption mechanisms. Here we use subband observations from the publicly available science verification (SV) data, while our methodology will also be applicable to regular ALMA data. The spectral resolving capacity of ALMA SV data is tested using the enhancement coincident with an X-ray bright point and from a plasmoid ejection event near active region NOAA12470 observed in Band 3 (84–116 GHz) on 2015 December 17. We compute the interferometric brightness temperature light curve for both features at each of the four constituent subbands to find the logarithmic millimeter spectrum. We compared the observed logarithmic spectral gradient with the derived relationship with optical thickness for an isothermal plasma to estimate the structures’ optical thicknesses. We conclude, within 90% confidence, that the stationary enhancement has an optical thickness between 0.02 ≤ τ ≤ 2.78, and that the moving enhancement has 0.11 ≤ τ ≤ 2.78, thus both lie near to the transition between optically thin and thick plasma at 100 GHz. From these estimates, isothermal plasmas with typical Band 3 background brightness temperatures would be expected to have electron temperatures of ∼7370–15300 K for the stationary enhancement and between ∼7440 and 9560 K for the moving enhancement, thus demonstrating the benefit of subband ALMA spectral analysis.

A Modified Spectral Conjugate Gradient Method with Global Convergence

In this paper, a modified version of the spectral conjugate gradient algorithm suggested by Jian, Chen, Jiang, Zeng and Yin is proposed. It is proved that the new method is globally convergent for ...

Spectral gradient of the thermal millimetre continuum as a diagnostic for optical thickness in the solar atmosphere (Corrigendum)

Aims. In this Letter we aim to show how the gradient of the thermal millimetre continuum spectrum, as emitted from the quiet solar atmosphere, may be used as a diagnostic for the optical thickness regime at the centre of the observing frequency band. Methods. We show the theoretical derivation of the gradient of the millimetre continuum for both logarithmic- and linear-scale spectra. We compare this expression with the empirical relationship between the slope of the millimetre continuum spectrum and the plasma optical thickness computed from both isothermal and multi-thermal two-dimensional cylindrical radiative transfer models. Results. It is found that the logarithmic-scale spectral gradient provides a clear diagnostic for the optical thickness regime for both isothermal and multi-thermal plasmas, provided that a suitable correction is made for a non-constant gaunt factor over the frequency band. For the use of observers we present values for this correction at all ALMA bands and at a wide range of electron temperatures. Conclusions. We find that the spectral gradient can be used to find (a) whether the source is fully optically thin, (b) the optical thickness of the source if it lies within the transitional regime between optically thin and thick plasma (τ ≈ 10−1−101), or (c) whether the source is fully optically thick for an isothermal plasma. A multi-thermal plasma will act the same as an isothermal plasma for case (a), however, the transitional regime will only extend from τ ≈ 10−1 to τ ≈ 100. Above τ = 1 the slope of the continuum will depend increasingly on the temperature gradient, as well as the optical thickness, reducing the reliability of the diagnostic.

174P/Echeclus and Its Blue Coma Observed Post-outburst

Abstract It has been suggested that centaurs may lose their red surfaces and become bluer due to the onset of cometary activity, but the way in which cometary outbursts affect the surface composition and albedo of active centaurs is poorly understood. We obtained consistent visual-near-infrared (VNIR) reflectance spectra of the sporadically active centaur 174P/Echeclus during a period of inactivity in 2014 and six weeks after its outburst in 2016 to see if activity had observably changed the surface properties of the nucleus. We observed no change in the surface reflectance properties of Echeclus following the outburst compared to before, indicating that, in this case, any surface changes due to cometary activity were not sufficiently large to be observable from Earth. Our spectra and post-outburst imaging have revealed, however, that the remaining dust coma is not only blue compared to Echeclus, but also bluer than solar, with a spectral gradient of −7.7 ± 0.6% per 0.1 μm measured through the 0.61–0.88 μm wavelength range that appears to continue up to λ ∼ 1.3 μm before becoming neutral. We conclude that the blue visual color of the dust is likely not a scattering effect, and instead may be indicative of the dust’s carbon-rich composition. Deposition of such blue, carbon-rich, comatic dust onto a red active centaur may be a mechanism by which its surface color could be neutralized.

Cluster-based spectral models for a robust assessment of soil properties

Soil spectroscopy has proven efficient for providing quantitative properties of soil chromophores and soil classification. However, building generic/global models might bring to light some problems which stem from the false premise that each chromophore affects all soil types equally. The objective of this paper is to offer a new cluster-based approach for the assessment of soil properties and compensating issues that might rise from using heterogeneous soil dataset. For that purpose, 1480 soil samples from varying climate zones in Israel were used. All samples were air-dried, ground, and sieved to 2 mm, then CaCO3, organic matter (OM), clay and sand contents together with the spectral signature were measured according to laboratory protocols. The spectral analysis was performed using the Spectral Angle Mapper (SAM) algorithm, spectral gradient and k-means clusters to split the dataset into distinct clusters. Subsequently, cluster-based models were performed and compared with generic models. Later, we focused on the OM parameter and applied the OM detection limit approach for a more robust and accurate assessment. Our results show that soil property prediction improved significantly when using the cluster-based models compared to the generic models and therefore should be considered when dealing with large and heterogeneous databases.

A Modified Spectral PRP Conjugate Gradient Projection Method for Solving Large‐Scale Monotone Equations and Its Application in Compressed Sensing

In this paper, we develop an algorithm to solve nonlinear system of monotone equations, which is a combination of a modified spectral PRP (Polak‐Ribière‐Polyak) conjugate gradient method and a projection method. The search direction in this algorithm is proved to be sufficiently descent for any line search rule. A line search strategy in the literature is modified such that a better step length is more easily obtained without the difficulty of choosing an appropriate weight in the original one. Global convergence of the algorithm is proved under mild assumptions. Numerical tests and preliminary application in recovering sparse signals indicate that the developed algorithm outperforms the state‐of‐the‐art similar algorithms available in the literature, especially for solving large‐scale problems and singular ones.

基于谱残差和梯度纹理融合特征的舰船检测

基于谱残差和梯度纹理融合特征的舰船检测

New hybrid algorithm based on nonmonotone spectral gradient and simultaneous perturbation

In this paper, we introduce a new hybrid method called nonmonotone spectral gradient and simultaneous perturbation (NSGSP). It combines the advantages of nonmonotone spectral gradient (NSG), and simultaneous perturbation (SP) methods. The main idea of our approach is to use the simultaneous perturbation (SP) method in order to get a non expensive estimate of the gradient, and exploit the good properties of the nonmonotone spectral gradient (NSG) method in order to compute an efficient line search. Several numerical experiments are provided. The results indicate that the new method is effective and outperforms most of other popular methods.