Spectral Details Research Articles

Benefits of extended high-frequency hearing for speech perception

Each species has a frequency range of hearing tailored to meet the demands placed upon that species by its environment and experiences. Why have humans retained extended high-frequency (EHF) hearing (i.e., sensitivity to acoustical energy beyond 8 kHz)? In this talk I will provide an overview of EHF hearing and report on studies from our lab revealing the utility of EHF hearing for speech perception. Historically, speech energy beyond 8 kHz was deemed unnecessary for applications of interest (i.e., telephony), and has often been neglected by tradition. We find, however, that speech energy beyond 13 kHz is audible for healthy young adult listeners. Access to EHFs improves (1) listeners' judgements of a talker's head orientation, and (2) speech recognition scores in a listening scenario where the target talker faces the listener, but masker talkers face away from the listener. This improvement is likely due to the directional nature of EHF energy radiating from the mouth and has some reliance on EHF spectral detail. Our results indicate that EHF energy in speech is audible and provides useful information about the speech signal.

Estimation of soil salt content using machine learning techniques based on remote-sensing fractional derivatives, a case study in the Ebinur Lake Wetland National Nature Reserve, Northwest China

AbstractSoil salinity is a common global environmental problem that severely restricts industrial, agricultural and human development. In Northwest China, soil salinity is a problem affecting the Lake Ebinur area and needs to be monitored and addressed. The use of optical remote sensing technology to for timely and accurate soil salinity monitoring has great potentials and can be crucial for industrial and agricultural development. Optical remote sensing technology is an important data source for monitoring of soil salinization because of its rich spectral information and high-resolution. Based on the HJ-1/HSI data of fractional derivative transformation, this paper calculated the parameters, such as the difference index (DI), normalized difference index (NDI), ratio index (RI) and salt index (SI), that can invert the spatial distribution of salinization and then calculates a particle swarm optimization support vector machine (PSO-SVM). These index parameters were used to invert the spatial distribution information of salinized soil to explore the influence of fractional derivatives on the estimation accuracy of the spatial distribution of salinized soil. The results showed the following: (1) The optimal model based on different fractional derivatives and exponential transformations was the DI transformation under the fractional derivative of 1.2-order. R2, RMSE and RPD were 0.66, 13.81 g/kg and 2.59, respectively. These data show that the model had a good prediction effect. (2) The application of fractional derivatives in the processing of hyperspectral images could highlight the spectral details of the images and enrich their pretreatment methods, which is of great significance for the inversion of the distribution of salinized soil by hyperspectral images. (3) In the lakeside area of Lake Ebinur, the soil is mainly saline to severely saline. In the northern mountainous region of Lake Ebinur, the soil is mainly non-saline soil to mildly saline soil. Finally, in the Kuitun riverbank area in the southeast of Lake Ebinur, the soils are mainly non-saline soils to mildly saline soils. The results of this study demonstrate the potential of spectral index optimization and prediction models to monitor soil salinization based on fractional derivatives. This technique highlights the spectral detail information and enriches the preprocessing methods of hyperspectral imagery, resulting in the mapping of the spatial distribution of the degree of soil salinization. This has practical importance for the prevention and control of soil salinization and its spatial extent in arid and semi-arid regions.

Multiscale building segmentation based on deep learning for remote sensing RGB images from different sensors

Building footprint segmentation from satellite and aerial images is an essential and challenging step for high-resolution building map generation. In urban management applications, such as building monitoring, infrastructure development, smart three-dimensional cities, and building change detection, building footprints are required to generate precise multiscale building maps. An efficient deep learning-based segmentation approach is proposed for multiscale building footprint extraction, and the results are presented for the most important challenges in photogrammetry and remote sensing, including shadows and occluded areas, vegetation covers, complex roofs, dense building areas, oblique images, and the generalization capability in different locations. The proposed method includes new dilated convolutional blocks containing kernels with different sizes to learn spectral–spatial relationships in multiscale satellite and aerial images with a high level of abstraction. The quantitative assessments of multiscale images from different locations with different spatial resolutions and spectral details show that the average F1 score and the average intersection over union for extracted footprints are about 86% and 76%, respectively. Compared with the state-of-the-art approaches, the proposed method has outstanding generalization capability and provides better performance for building footprint segmentation from multisensor single images.

Fast Discrete Curvelet Decomposition with Gradient Fusion Based Technique for Pansharpening of Multispectral Image.

The Process of improving the local details of multispectral image using the information captured from different image sensors is called pansharpening. In this paper, the information captured from three different satellite sensors such as synthetic aperture radar (SAR), multispectral (MS) and panchromatic (PAN) of the same scene is combined effectively using the proposed a novel approach. This approach consists of three stages of fusion scheme, where block based wrap fast discrete curvelet decomposition technique is used in the first stage, which results an intermediate PAN image that contain the almost complete spatial details of SAR image and nonadaptive sparse details of the SAR and PAN images. Next the gradient fusion based technique is used to combine the SAR and an intermediate PAN image which retains more information in the PAN-SAR (PS) fused image. Further pansharpening of the MS image is accomplished by using the hybrid technique which is based on injecting the primary and secondary high frequency components into the original MS image. In this algorithm, the high frequency details are extracted by taking difference between the PS and synthesized intensity image, then the extracted high frequency details are modulated by local adaptive and post processing fusion parameter. The experimental outputs reveal that the proposed algorithm results better performance than other techniques by providing more spatial information in multispectral image while preserving the spectral details and has the potential characterize urban areas in a fused image in a better way.

Monitoring early stage invasion of exotic Spartina alterniflora using deep-learning super-resolution techniques based on multisource high-resolution satellite imagery: A case study in the Yellow River Delta, China

Over the past decades, Spartina alterniflora, one of the top exotic invasive plants in China, has expanded throughout coastal China. In the Yellow River Delta (YRD), the rapid expansion of S. alterniflora has caused serious negative ecological effects. Current studies have concentrated primarily on mapping the distribution of S. alterniflora with medium-resolution satellite imagery at the regional or landscape scale, which have a limited capability in early detection and monitoring of the invasive process at the patch scale. In this study, we proposed a framework for monitoring the early stage invasion of S. alterniflora patches in the YRD using multiyear multisource high-spatial-resolution satellite imagery with various ground sampling distances (WorldView-2, SPOT-6, GaoFen-1, GaoFen-2, and GaoFen-6 from 2012 to 2019). First, we proposed to use deep-learning-based image super-resolution models to enhance all images to submeter (0.5 m) resolution. Then, we adopted stepwise evolution analysis-based image segmentation and object-based classification rules to detect and delineate S. alterniflora patches from the super-resolved imagery. By investigating Super-Resolution Convolutional Neural Networks (SRCNN) and Fast Super-Resolution Convolutional Neural Networks (FSRCNN) and comparing these methods with the conventional bicubic interpolation method for image resolution enhancement, we concluded that FSRCNN was superior in constructing spectral and structural details from the 1 m/1.5 m/2 m resolution images to 0.5 m resolution. FSRCNN, in particular, was more effective and efficient in discerning and estimating the size of small S. alterniflora patches (<50 m2). Using our method, 76 of 83 field-measured small patches were accurately detected and the delineated S. alterniflora patch perimeters agreed well with the field-measured patch perimeters (root mean square error [RMSE] = 8.29 m, mean absolute percentage error [MAPE] = 23.46 %). The invasion process showed fast expansion from 2012 to 2015 and slow growth from 2016 to 2019. We observed that the landward limits of S. alterniflora patches were influenced by elevation and vicinity to tidal creeks.

Adaptive Contrast Enhancement of Optical Imagery Based on Level of Detail (LOD)

The viewing and sharing of remote sensing optical imagery through the World Wide Web is an efficient means for providing information to the general public and decision makers. Since clouds and hazes inevitably limit the contrast and deteriorate visual effects, only cloudless scenes are usually included and presented in existing web mapping services. This work proposes a level-of-detail (LOD) based enhancement approach to present satellite imagery with an adaptively enhanced contrast determined by its viewing LOD. Compared to existing web mapping services, this new approach provides a better visual effect as well as spectral details of satellite imagery for cases partially covered with clouds or cirrocumulus clouds. The full archive of global satellite imagery, either the existing one or the one collected in the future, can be utilized and shared through the Web with the processing proposed in this new approach.

Non‐Spectator Feature of α‐Diimine Mimicked Di/tetrahydro‐bisisoquinoline and Biimidazopyridine on {Ru(acac)2} Platform

The diverse reactivity profiles of α‐diimine analogues, di/tetrahydro‐bisisoquiniline (L1–2H/L2–4H), and biimidazopyridine (L3) have been illustrated on the {Ru(acac)2} platform (acac=acetylacetonate). Increasing Lewis acidity on coordination to {Ru(acac)2} as well as fine tuning of ligand backbone led to oxidative dehydrogenation of L1–2H/L2–4H to yield metallated bisisoquinoline (BIQ) and unexplored C–C coupling/nucleophilic attack assisted ring opening reactions at L3 to rearrange into modified metallated amidate functions. Preformed L1–4H involving amine functionality underwent in situ transformation to imine in [RuII/III(acac)2(L1–2H)]n+ (1n+, n = 0,1), followed by dehydrogenation to yield [RuII/III(acac)2(BIQ)]n+ (2n+, n = 0,1). Analogous complex [RuII(acac)2(L2–4H)]n+ (3n+, n = 0,1) encompassing diimine (L2–4H) ligand also participated in similar dehydrogenation process to generate 2n+. On the other hand, reaction of L3 with {Ru(acac)2} led to the simultaneous formation of three products: [RuIII(acac)2(L3)]ClO4 ([4]ClO4) incorporating unperturbed N,N‐donating L3 and [RuIII(acac)(L4)] (5)/[RuIII(acac)2(L5)] (6) with functionalized L3. Involvement of intramolecular C–C coupling between the methine carbon of acac and L3 or the intermolecular nucleophilic attack of H2O at L3, resulted in functionalized L4 or L5 in 5 or 6, respectively. Authentication of the isolated products was made via structural, electrochemical and spectral details along with theoretical support.

Normal Aging: Alterations in Scalp EEG Using Broadband and Band-Resolved Topographic Maps

Given the majority of age-related diseases have been described as disconnection syndromes, understanding the functional connections of normal aging is of considerable importance. Here, an EEG-based scalp level analysis has been performed to identify the alterations in the synchronized brain regions in aged, compared to young persons. Two groups, aged and young subjects were studied, each consisting of 18 participants. First, conventionally extracted broadband topographic maps, also called microstate maps, were examined. The results showed an overall dominant alteration: a uniform decrease in synchronization of brain regions related to cognitive processing resources that was observed only when the maps C and D were characterized in temporal parameters. However, no remarkable change in the spatial distribution was found between the groups. This failure in identifying differences in the spatial distribution was hypothesized to be due to the presence of superimposed signals of several frequencies in the broadband signal that is used for the extraction of microstate maps. Second, spectrally resolved band-wise topographic maps, which we have shown, in a previous study, are able to detect spectral details associated with broadband microstates maps, were used to address this failure. The use of the instantaneous frequency concept is essential in the extraction of band-wise topographic maps, and represents a novelty compared to current studies. The method consists of three steps: (a) from EEG signal, the Empirical Mode Decomposition method is used to extract underlying oscillatory components; (b) these intrinsic oscillatory components are then amplitude demodulated and subjected to numerical equations for the calculation of instantaneous features, such as amplitude, and frequency; finally, (c) based on these instantaneous features, band-wise topographic maps are extracted. Here, as a first application to aging data, these band-wise topographic maps have shown the capability of capturing the age-related changes in both spatial distributions, and in temporal characterization. Spatially, the potential distribution in the aged and the young subject groups, respectively, showed differences, while, in temporal characterization, both increases and decreases were observed, suggesting the lengths of synchronized activities vary differentially, and in accordance with results from fMRI studies. These observed differences also support the dedifferentiation and compensation mechanisms.

Dynamic current steering with phantom electrode in cochlear implants

Phantom electrode (PE) stimulation can extend the lower limit of pitch perception with cochlear implants (CIs) by using simultaneous out-of-phase stimulation of the most apical primary electrode and the adjacent basal compensating electrode. The total electrical field may push the excitation pattern beyond the most apical electrode to elicit a lower pitch, depending on the ratio of current between the compensating and primary electrodes (i.e., the compensation coefficient σ). This study tested the hypothesis that dynamic current steering of PE stimuli can be implemented by varying σ over time to encode spectral details in low frequencies. To determine the range of σ for current steering and the corresponding current levels, Experiment 1 tested CI users’ loudness balance and pitch ranking of static PE stimuli with σ from 0 to 0.6 in steps of 0.2. It was found that the equal-loudness most comfortable level significantly increased with σ and can be modeled by a piecewise linear function of σ. Consistent with the previous findings, higher σ elicited either lower or similar pitches without salient pitch reversals than lower σ. Based on the results of Experiment 1, Experiment 2 created flat, rising, and falling pitch contours of 300–1000 ms using dynamic PE stimuli with time-varying σ from 0 to 0.6 and equal-loudness current levels. In a pitch contour identification (PCI) task, CI users scored 80% and above on average. Increasing the stimulus duration from 300 to 1000 ms slightly but did not significantly improve the PCI scores. Across subjects, the 1000-ms PCI scores in Experiment 2 were significantly correlated with the cumulative pitch-ranking sensitivity in Experiment 1. It is thus feasible to use dynamic current steering with PE to encode low-frequency pitch cues for CI users.

Errors in PV power modelling due to the lack of spectral and angular details of solar irradiance inputs

Photovoltaic (PV) modules are sensitive to the spectral distribution of solar irradiance. Although numerical weather prediction (NWP) models compute irradiance in several spectral bands, only broadband quantities are provided in the standard outputs used for PV forecasts. This study investigates how this lack of information impacts PV power estimation. A physical PV model is first designed that accounts for the spectral distribution of irradiance and the spectral response of the panels. This model is evaluated using measurements performed at Site Instrumental de Recherche par Télédétection Atmosphérique (SIRTA), Palaiseau, France. The mean relative difference between simulated and measured PV power for a monocrystalline silicon module of 250 W nominal power is −0.9%, and the mean bias is −2.0 W. This model is then used to investigate the impact of solar zenith angle and clouds on the performance of PV modules. PV performance can increase in cloudy conditions by 5% through spectral filtering of near-infrared irradiance, and by 18% when only the useful irradiance is considered to compute performance. This spectral effects is not captured by the PV model when only broadband irradiances are used. In such case errors up to 15% are obtained in simulated PV power compared to using a state-of-the-art NWP model providing irradiance in 14 spectral bands. More generally, broadband global horizontal irradiance appears insufficient for accurate PV power modelling, highlighting the added value of spectrally-and-angularly-refined irradiances. This stresses that PV models should use more detailed irradiance inputs, which could be easily achieved by exploiting internal variables of NWP models.

Reconstruction of spectra from truncated free induction decays by deep learning in proton magnetic resonance spectroscopy.

To explore the applicability of convolutional neural networks (CNNs) in the reconstruction of spectra from truncated FIDs (tFIDs) in 1 H-MRS, which can be valuable in situations in which data sampling is highly limited, such as spectroscopic magnetic resonance fingerprinting. Rat brain FIDs were simulated at 9.4 T based on in vivo data (N = 11) and randomly truncated by retaining 8, 16, 32, 64, 128, 256, 512, and 1024 (null truncation) points (denoted as tFID8 , tFID16 , … tFID1024 ). Using a U-net, 3 CNNs were individually trained (N = 40 000) in time domain only (FID to FID [FID CNNFID ]), in frequency domain only (spectrum to spectrum [spec CNNspec ]), and across the domains (FID to spectrum [FID CNNspec ]) to map the truncated data to their fully sampled versions. The CNNs were tested on the simulated data (N = 5000), and the CNN with the best performance was further tested on the in vivo data, for which the CNN-predicted fully sampled data were analyzed using the LCModel and the results were compared with those from the original, fully sampled data. The best result on the simulated data was obtained with spec CNNspec , which effectively recovered the spectral details even for those input spectra that appear as a hump due to substantial FID truncation (spectra from tFID16 and tFID32 ). Overall, its performance was significantly degraded on the in vivo data. Nonetheless, using spec CNNspec , several coupled spins in addition to the major singlets can be quantified from tFID128 with the error no larger than 10%. Upon the availability of more realistically simulated training data, CNNs can also be used in the reconstruction of spectra from truncated FIDs.

UV Fluorescence Imagery of the Turin Shroud – Digitally Revisited

UV fluorescence imagery of faint or fragile images and markings (writing) on relics and artwork is an affordable non-destructive tool useful in revealing often invisible details and in monitoring temporal stability. UV fluorescent images of Turin were recorded for the first time during the 1978 scientific investigation. The original images were recorded on color film and now have been digitally scanned and enhanced using image processing software. The processed UV images contain spectral discriminatory information and high spatial detail resolution with high contrast that is not discernable in white light images. Differing fluorescent emission colors were found to be associated with image features such as body image, burns, blood flows, skin wounds and water flows on the Turin Shroud. Spectral signature information has the potential for assisting the determination or elimination of the causes responsible for the appearance of the various features. Inconsistent and non-uniform exposure problems in the original photography were revealed, and their impact the subsequent digitization of the images is discussed. Suggestions for future fluorescence image collection include the design of equipment that will eliminate the problems associated illumination and film exposure. The extraction of spectral and spatial detail through UV fluorescence imagery is relevant to tracking temporal and climatic changes to assist conservation efforts.

Shallow–Deep Convolutional Network and Spectral-Discrimination-Based Detail Injection for Multispectral Imagery Pan-Sharpening

Pan-sharpening is a significant task in remote sensing image processing, which merges a high-resolution panchromatic (PAN) image and a low-resolution multispectral (MS) image to create a high-resolution MS image. In this article, we propose a novel deep-learning-based MS image pan-sharpening method that combines a shallow–deep convolutional network (SDCN) and a spectral discrimination-based detail injection (SDDI) model. SDCN consists of a shallow network and a deep network, which can capture mid-level and high-level spatial features from PAN images. SDDI, inspired by the “Amelioration de la Resolution Spatial par Injection de Structures” concept, is developed to merge the spatial details extracted by SDCN into MS images with minimal spectral distortion. SDCN and SDDI are collaboratively learned for achieving high-spatial-resolution MS image and preserving more spectral information. Both the visual assessment and the quantitative assessment results on IKONOS and QuickBird datasets confirmed that the proposed method outperforms several state-of-the-art pan-sharpening methods.

Extended High Frequencies Provide Both Spectral and Temporal Information to Improve Speech-in-Speech Recognition.

Several studies have demonstrated that extended high frequencies (EHFs; >8 kHz) in speech are not only audible but also have some utility for speech recognition, including for speech-in-speech recognition when maskers are facing away from the listener. However, the contribution of EHF spectral versus temporal information to speech recognition is unknown. Here, we show that access to EHF temporal information improved speech-in-speech recognition relative to speech bandlimited at 8 kHz but that additional access to EHF spectral detail provided an additional small but significant benefit. Results suggest that both EHF spectral structure and the temporal envelope contribute to the observed EHF benefit. Speech recognition performance was quite sensitive to masker head orientation, with a rotation of only 15° providing a highly significant benefit. An exploratory analysis indicated that pure-tone thresholds at EHFs are better predictors of speech recognition performance than low-frequency pure-tone thresholds.

Accommodation of gender-related phonetic differences by listeners with cochlear implants and in a variety of vocoder simulations

Speech perception requires accommodation of a wide range of acoustic variability across talkers. A classic example is the perception of "sh" and "s" fricative sounds, which are categorized according to spectral details of the consonant itself, and also by the context of the voice producing it. Because women's and men's voices occupy different frequency ranges, a listener is required to make a corresponding adjustment of acoustic-phonetic category space for these phonemes when hearing different talkers. This pattern is commonplace in everyday speech communication, and yet might not be captured in accuracy scores for whole words, especially when word lists are spoken by a single talker. Phonetic accommodation for fricatives "s" and "sh" was measured in 20 cochlear implant (CI) users and in a variety of vocoder simulations, including those with noise carriers with and without peak picking, simulated spread of excitation, and pulsatile carriers. CI listeners showed strong phonetic accommodation as a group. Each vocoder produced phonetic accommodation except the 8-channel noise vocoder, despite its historically good match with CI users in word intelligibility. Phonetic accommodation is largely independent of linguistic factors and thus might offer information complementary to speech intelligibility tests which are partially affected by language processing.

Modeling perceived externalization of a static, lateral sound image

Perceived externalization is a relevant feature to create an immersive acoustic environment with headphone reproduction. In the present study, listener-specific acoustic transfer characteristics for an azimuth angle of 90° were modified to investigate the role of monaural spectral cues, interaural level differences (ILDs), and temporal fluctuations of ILDs on perceived externalization in anechoic and reverberant environments. Listeners’ ratings suggested that each acoustic cue was important for perceived externalization. If only one correct acoustic cue remained in the ear signals, the sound image could not be perceived as fully externalized. Reverberation did reduce but not eliminate the influences of monaural spectral and ILD cues on perceived externalization. Additionally, the spectral details of the ipsilateral ear signal were more important for perceived externalization than those in the contralateral ear signal. A computational model was proposed to quantify those relationships and predict externalization ratings by comparing the acoustic cues extracted from the target (modified) and template (non-processed) binaural signals after several auditory processing steps. The accuracy of predicted externalization ratings was higher than 90% under all experimental conditions.

Prediction of soil organic matter in northwestern China using fractional-order derivative spectroscopy and modified normalized difference indices

Visible-near-infrared (Vis-NIR) spectroscopy makes it possible to estimate soil organic matter content (SOMC). Spectral pretreatment techniques have important significance in the quantitative analysis of SOMC. A total of 150 soil samples collected in northwestern China were used as data sets for calibration and validation. The SOMC values and reflectance spectra were measured in the laboratory. Fractional-order derivatives (FODs) (intervals of 0.05, range of 0–2) were used for soil spectral pretreatment, and a new three-band index (modified normalized difference index, MNDI) was constructed based on the band-optimization algorithm and the existing two-band exponential form (normalized difference index, NDI). Partial least square-support vector machine (PLS-SVM) models were calibrated using spectral parameters selected based on a single dimension (FOD), two-dimensional index (NDI) and three-dimensional index (MNDI) and subsequently applied to estimate SOMC. Three model evaluation parameters, namely, the coefficient of determination (R2), root mean squared error (RMSE), and ratio of performance to interquartile range (RPIQ), were used to evaluate the estimation accuracy of the models. The results showed that with increased derivative order, the spectral strength gradually decreased, but the spectral detail increased. Furthermore, the correlation between FOD spectra and SOMC was significantly enhanced in the visible region, with the most obvious effect in the 1.05- to 1.45-order range. The PLS-SVM modeling results showed that the sensitivity and estimation accuracy of SOMC increased with increasing spectral synergy (i.e., 1D (FOD) < 2D (NDI) < 3D (MNDI)). Among the models, MNDI exhibited the best model performance, yielding a validation R2 and RPIQ of 0.846 and 3.396, respectively. The combination of FOD and MNDI could weaken the soil noise information and improve the prediction accuracy of SOMC. Furthermore, the three-dimensional index has strong application potential for estimating other biochemical parameters of soil using Vis-NIR spectroscopy.

Hyperspectral image recovery based on fusion of coded aperture snapshot spectral imaging and RGB images by guided filtering

In compressive hyperspectral imaging systems, the key issue is to accurately and efficiently recover 3D spectral data cubes from spectrally undersampled 2D projections. In this paper, a guided-filtering-based reconstruction algorithm is proposed to recover hyperspectral data cube with a joint coded aperture snapshot spectral imaging (CASSI) and RGB imaging system. In order to preserve the spectral and spatial details in the CASSI and the RGB branch to the greatest extent, 2D projections from the two branches are reconstructed into hyperspectral images individually with a pre-trained dictionary. The appropriate combination of the information from the two branches is realized through guided image filtering, which is applied on the reconstruction result of the CASSI branch with the result from the RGB branch as the guidance image. In virtue of combination between the well-performed spectral fidelity of the CASSI branch and the high spatial accuracy of the RGB branch, the proposed guided-filtering-based compressive hyperspectral imaging (GFCHI) not only overcomes the block effect and blurring common in the state-of-the-art methods, but also improves spectral fidelity related to key applications of hyperspectral imaging. The quantitative metrics associated with spatial, spectral and overall performance demonstrate the state-of-the-art reconstruction quality of GFCHI.

Stability analysis of ground states in a one-dimensional trapped spin-1 Bose gas

In this work we study the stability properties of the ground states of a spin-1 Bose gas in presence of a trapping potential in one spatial dimension. To set the stage we first map out the phase diagram for the trapped system by making use of a, so-called, continuous-time Nesterov method. We present an extension of the method, which has been previously applied to one-component systems, to our multi-component system. We show that it is a powerful and robust tool for finding the ground states of a physical system without the need of an accurate initial guess. We subsequently solve numerically the Bogoliubov de-Gennes equations in order to analyze the stability of the ground states of the trapped spin-1 system. We find that the trapping potential retains the overall structure of the stability diagram, while affecting the spectral details of each of the possible ground state waveforms. It is also found that the peak density of the trapped system is the characteristic quantity describing dynamical instabilities in the system. Therefore replacing the homogeneous density with the peak density of the trapped system leads to good agreement of the homogeneous Bogoliubov predictions with the numerically observed maximal growth rates of dynamically unstable modes. The stability conclusions in the one-dimensional trapped system are independent of the spin coupling strength and the normalized trap strength over several orders of magnitude of their variation.

DDLPS: Detail-Based Deep Laplacian Pansharpening for Hyperspectral Imagery

In this paper, we propose a new pansharpening method called detail-based deep Laplacian pansharpening (DDLPS) to improve the spatial resolution of hyperspectral imagery. This method includes three main components: upsampling, detail injection, and optimization. In particular, a deep Laplacian pyramid super-resolution network (LapSRN) improves the resolution of each band. Then, a guided image filter and a gain matrix are used to combine the spatial and spectral details with an optimization problem, which is formed to adaptively select an injection coefficient. The DDLPS method is compared with 11 state-of-the-art or traditional pansharpening approaches. The experimental results demonstrate the superiority of the DDLPS method in terms of both quantitative indices and visual appearance. In addition, the training of LapSRN is based on the data sets of traditional RGB images, which overcomes the practical difficulty of insufficient training samples for pansharpening.