Spatial Pixel Research Articles

A Fast Spatial–Spectral Preprocessing Module for Hyperspectral Endmember Extraction

Mixed-pixel decomposition of a hyperspectral image is developed on the basis of extracting unique constituent elements known as endmembers (EMs) and their abundance fraction estimation. Recently, integration of spatial content and spectral information is applied by means of several preprocessing modules (PPs) with the purpose of improving EM extraction (EE) accuracy and decreasing EE time. In this letter, a fast spatial–spectral preprocessing module is proposed, which determines the spectral purity score of pixels located at spatially homogenous regions. These homogenous regions including not spatial border pixels are identified using unsupervised k-means clustering technique and spatial neighborhood system. Afterward, a fraction of homogenous pixels (usually half) with greater spectral purity score is adopted as the best EM candidates for subsequent EEs. This novel PP is examined on synthetic and real AVIRIS data sets, which demonstrates its worthy performance in terms of accuracy and fast computation time.

Vector Attribute Profiles for Hyperspectral Image Classification

Morphological attribute profiles are among the most prominent spectral–spatial pixel description methods. They are efficient, effective, and highly customizable multiscale tools based on hierarchical representations of a scalar input image. Their application to multivariate images in general and hyperspectral images in particular has been so far conducted using the marginal strategy , i.e., by processing each image band (eventually obtained through a dimension reduction technique) independently. In this paper, we investigate the alternative vector strategy , which consists in processing the available image bands simultaneously. The vector strategy is based on a vector-ordering relation that leads to the computation of a single max and min tree per hyperspectral data set, from which attribute profiles can then be computed as usual. We explore known vector-ordering relations for constructing such max trees and, subsequently, vector attribute profiles and introduce a combination of marginal and vector strategies. We provide an experimental comparison of these approaches in the context of hyperspectral classification with common data sets, where the proposed approach outperforms the widely used marginal strategy.

MEASUREMENT OF THE BLACK HOLE MASS IN NGC 1332 FROM ALMA OBSERVATIONS AT 0.044 ARCSECOND RESOLUTION

ABSTRACT We present Atacama Large Millimeter/submillimeter Array (ALMA) Cycle 3 observations of CO(2–1) emission from the circumnuclear disk in the E/S0 galaxy NGC 1332 at 0.″044 resolution. The disk exhibits regular rotational kinematics and central high-velocity emission (±500 km s−1) consistent with the presence of a compact central mass. We construct models for a thin, dynamically cold disk in the gravitational potential of the host galaxy and black hole and fit the beam-smeared model line profiles directly to the ALMA data cube. Model fits successfully reproduce the disk kinematics out to r = 200 pc. Fitting models just to spatial pixels within projected r = 50 pc of the nucleus (two times larger than the black hole’s gravitational radius of influence), we find M BH = ( 6.64 − 0.63 + 0.65 ) × 10 8 M ⊙ . This observation demonstrates ALMA’s powerful capability to determine the masses of supermassive black holes by resolving gas kinematics on small angular scales in galaxy nuclei.

Enhanced-Resolution Reconstruction of ASCAT Backscatter Measurements

A wind scatterometer measures the normalized radar cross section σ° of the Earth's surface in order to estimate the ocean near-surface wind. Each measurement is the weighted integral of σ° over an area, so samples of σ° are filtered by a measurement spatial response function (SRF). Enhanced-resolution σ° data may be produced from σ° measurements and their associated SRF using image reconstruction algorithms. These enhancedresolution data products are useful for land and ice studies in addition to high-resolution ocean wind retrieval. The spatial resolution and noise may be improved by combining data for multiple passes, trading off temporal resolution for spatial resolution. In this paper, we consider the production of enhanced-resolution σ° image reconstruction from the Advanced Scatterometer (ASCAT) on the MetOp satellites for land and ice regions. Two previously introduced image reconstruction algorithms, the weighted AVErage (AVE) and Scatterometer Image Reconstruction (SIR), are applied to ASCAT data, as well as a conventional gridding method. We select optimum values of the reconstruction parameters for ASCAT. The imaging algorithms are compared and evaluated, as quantified by the spatial resolution, pixel mean and variance, and pixel correlation of the produced images. The effective spatial resolution of the AVE and SIR reconstructed images is on the order of 15 to 20 km, an improvement over the nominal spatial resolution of 25 and 50 km for the spatially averaged swath-oriented ASCAT L1B products.

Hot prominence detected in the core of a coronal mass ejection: Analysis of SOHO/UVCS Lαand SOHO/LASCO visible-light observations

Context. The paper deals with the physics of erupting prominences in the core of coronal mass ejections (CME). Aims. We determine the physical parameters of an erupting prominence embedded in the core of a CME using SOHO/UVCS hydrogen L α and L β lines and SOHO/LASCO visible light observations. In particular we analyze the CME event observed on August 2, 2000. We develop the non-LTE (NLTE; i.e. considering departures from the local thermodynamic equilibrium – LTE) spectral diagnostics based on L α and visible light observations. Methods. Our method is based on 1D NLTE modeling of eruptive prominences and takes into account the effect of large flow velocities, which reach up to 300 km s -1 for the studied event (the so-called Doppler dimming). The NLTE radiative-transfer method can be used for both optically thin and thick prominence structures. We combine spectroscopic UVCS observations of an erupting prominence in the core of a CME with visible light images from LASCO-C2 in order to derive the geometrical parameters like projected thickness and velocity, together with the effective temperature and column density of electrons. These are then used to constrain our NLTE radiative transfer modeling which provides the kinetic temperature, microturbulent velocity, gas pressure, ionization degree, the line opacities, and the prominence effective thickness (geometrical filling factor). Results. Analysis was made for 69 observational points (spatial pixels) inside the whole erupting prominence. Roughly one-half of them show a non-negligible L α optical thickness for flow velocity 300 km s -1 and about one-third for flow velocity 150 km s -1 . All pixels with L α τ 0 ≤ 0.3 have been considered for further analysis, which is presented in the form of statistical distributions (histograms) of various physical quantities such as the kinetic temperature, gas pressure, and electron density for two representative flow velocities (150 and 300 km s -1 ) and non-zero microturbulence. For two pixels co-temporal LASCO visible-light data are also available, which further constrains the diagnostics of the electron density and effective thickness. Detailed NLTE modeling is presented for various sets of input parameters. Conclusions. The studied CME event shows that the erupting prominence expands to large volumes, meaning that it is a low-pressure structure with low electron densities and high temperatures. This analysis provides a basis for future diagnostics using the METIS coronagraph on board the Solar Orbiter mission.

Spatial scale and pattern dependences of aboveground biomass estimation from satellite images: a case study of the Sierra National Forest, California

Context Spatial scale and pattern play important roles in forest aboveground biomass (AGB) estimation in remote sensing. Changes in the accuracy of satellite images-estimated forest AGBs against spatial scales and pixel distribution patterns has not been evaluated, because it requires ground-truth AGBs of fine resolution over a large extent, and such data are difficult to obtain using traditional ground surveying methods.

Unmanned aerial vehicle canopy reflectance data detects potassium deficiency and green peach aphid susceptibility in canola

There is growing evidence that potassium deficiency in crop plants increases their susceptibility to herbivorous arthropods. The ability to remotely detect potassium deficiency in plants would be advantageous in targeting arthropod sampling and spatially optimizing potassium fertilizer to reduce yield loss due to the arthropod infestations. Four potassium fertilizer regimes were established in field plots of canola, with soil and plant nutrient concentrations tested on three occasions: 69 (seedling), 96 (stem elongation), and 113 (early flowering) days after sowing (DAS). On these dates, unmanned aerial vehicle (UAV) multi-spectral images of each plot were acquired at 15 and 120 m above ground achieving spatial (pixel) resolutions of 8.1 and 65 mm, respectively. At 69 and 96 DAS, field plants were transported to a laboratory with controlled lighting and imaged with a 240-band (390–890 nm) hyperspectral camera. At 113 DAS, all plots had become naturally infested with green peach aphids (Hemiptera: Aphididae), and intensive aphid counts were conducted. Potassium deficiency caused significant: (1) increase in concentrations of nitrogen in youngest mature leaves, (2) increase in green peach aphid density, (3) decrease in vegetation cover, (4) decrease in normalized difference vegetation indices (NDVI) and decrease in canola seed yield. UAV imagery with 65 mm spatial resolution showed higher classification accuracy (72–100 %) than airborne imagery with 8 mm resolution (69–94 %), and bench top hyperspectral imagery acquired from field plants in laboratory conditions (78–88 %). When non-leaf pixels were removed from the UAV data, classification accuracies increased for 8 mm and 65 mm resolution images acquired 96 and 113 DAS. The study supports findings that UAV-acquired imagery has potential to identify regions containing nutrient deficiency and likely increased arthropod performance.

Fast implementation of kernel simplex volume analysis based on modified Cholesky factorization for endmember extraction

Endmember extraction is a key step in the hyperspectral image analysis process. The kernel new simplex growing algorithm (KNSGA), recently developed as a nonlinear alternative to the simplex growing algorithm (SGA), has proven a promising endmember extraction technique. However, KNSGA still suffers from two issues limiting its application. First, its random initialization leads to inconsistency in final results; second, excessive computation is caused by the iterations of a simplex volume calculation. To solve the first issue, the spatial pixel purity index (SPPI) method is used in this study to extract the first endmember, eliminating the initialization dependence. A novel approach tackles the second issue by initially using a modified Cholesky factorization to decompose the volume matrix into triangular matrices, in order to avoid directly computing the determinant tautologically in the simplex volume formula. Theoretical analysis and experiments on both simulated and real spectral data demonstrate that the proposed algorithm significantly reduces computational complexity, and runs faster than the original algorithm.

Hypergraph-Regularized Sparse NMF for Hyperspectral Unmixing

Hyperspectral image (HSI) unmixing has attracted increasing research interests in recent decades. The major difficulty of it lies in that the endmembers and the associated abundances need to be separated from highly mixed observation data with few a priori information. Recently, sparsity-constrained nonnegative matrix factorization (NMF) algorithms have been proved effective for hyperspectral unmixing (HU) since they can sufficiently utilize the sparsity property of HSIs. In order to improve the performance of NMF-based unmixing approaches, spectral and spatial constrains have been added into the unmixing model, but spectral–spatial joint structure is required to be more accurately estimated. To exploit the property that similar pixels within a small spatial neighborhood have higher possibility to share similar abundances, hypergraph structure is employed to capture the similarity relationship among the spatial nearby pixels. In the construction of a hypergraph, each pixel is taken as a vertex of the hypergraph, and each vertex with its $k$ nearest spatial neighboring pixels form a hyperedge. Using the hypergraph, the pixels with similar abundances can be accurately found, which enables the unmixing algorithm to obtain promising results. Experiments on synthetic data and real HSIs are conducted to investigate the performance of the proposed algorithm. The superiority of the proposed algorithm is demonstrated by comparing it with some state-of-the-art methods.

Spectral–Spatial Adaptive Sparse Representation for Hyperspectral Image Denoising

In this paper, a novel spectral–spatial adaptive sparse representation (SSASR) method is proposed for hyperspectral image (HSI) denoising. The proposed SSASR method aims at improving noise-free estimation for noisy HSI by making full use of highly correlated spectral information and highly similar spatial information via sparse representation, which consists of the following three steps. First, according to spectral correlation across bands, the HSI is partitioned into several nonoverlapping band subsets. Each band subset contains multiple continuous bands with highly similar spectral characteristics. Then, within each band subset, shape-adaptive local regions consisting of spatially similar pixels are searched in spatial domain. This way, spectral–spatial similar pixels can be grouped. Finally, the highly correlated and similar spectral–spatial information in each group is effectively used via the joint sparse coding, in order to generate better noise-free estimation. The proposed SSASR method is evaluated by different objective metrics in both real and simulated experiments. The numerical and visual comparison results demonstrate the effectiveness and superiority of the proposed method.

Resolved Gas Kinematics in a Sample of Low-Redshift High Star-Formation Rate Galaxies

AbstractWe have used integral field spectroscopy of a sample of six nearby (z ~ 0.01–0.04) high star-formation rate ($\text{SFR} \sim 10\hbox{--}40$$\text{M}_\odot \text{ yr$^{-1}$}$) galaxies to investigate the relationship between local velocity dispersion and star-formation rate on sub-galactic scales. The low-redshift mitigates, to some extent, the effect of beam smearing which artificially inflates the measured dispersion as it combines regions with different line-of-sight velocities into a single spatial pixel. We compare the parametric maps of the velocity dispersion with the Hα flux (a proxy for local star-formation rate), and the velocity gradient (a proxy for the local effect of beam smearing). We find, even for these very nearby galaxies, the Hα velocity dispersion correlates more strongly with velocity gradient than with Hα flux—implying that beam smearing is still having a significant effect on the velocity dispersion measurements. We obtain a first-order non parametric correction for the unweighted and flux weighted mean velocity dispersion by fitting a 2D linear regression model to the spaxel-by-spaxel data where the velocity gradient and the Hα flux are the independent variables and the velocity dispersion is the dependent variable; and then extrapolating to zero velocity gradient. The corrected velocity dispersions are a factor of ~ 1.3–4.5 and ~ 1.3–2.7 lower than the uncorrected flux-weighted and unweighted mean line-of-sight velocity dispersion values, respectively. These corrections are larger than has been previously cited using disc models of the velocity and velocity dispersion field to correct for beam smearing. The corrected flux-weighted velocity dispersion values are σm ~ 20–50 km s−1.

Optical Demonstration of THz, Dual-Polarization Sensitive Microwave Kinetic Inductance Detectors

The next generation BLAST experiment (BLAST-TNG) is a suborbital balloon payload that seeks to map polarized dust emission in the 250 $\mu$m, 350 $\mu$m and 500 $\mu$m wavebands. The instrument utilizes a stepped half-wave plate to reduce systematics. The general requirement of the detectors is that they are photon-noise-limited and dual-polarization sensitive. To achieve this goal, we are developing three monolithic arrays of cryogenic sensors, one for each waveband. Each array is feedhorn-coupled and each spatial pixel consists of two orthogonally spaced polarization-sensitive microwave kinetic inductance detectors (MKIDs) fabricated from a Ti/TiN multilayer film. In previous work, we demonstrated photon-noise-limited sensitivity in 250 $\mu$m waveband single polarization devices. In this work, we present the first results of dual-polarization sensitive MKIDs at 250 $\mu$m.

Mixed Pixel Wise Characterization Based on HDP-HMM and Hyperspectral Image Shape Detection using Hybrid Canny Edge Detection and WPDF

Hyper Spectral Imaging (HSI) gathers and processes information from across the electromagnetic spectrum. The information enclosed in hyperspectral data permits the characterization, recognition and classification of the land-covers with enhanced accuracy and robustness. On the other hand, quite a lot of vital complications must be considered during the classification process of hyperspectral data, among which the maximum quantity of spectral channels, the spatial unevenness of the spectral signature, shape discovery of the images and the value of data. Above all, the maximum quantity of spectral channels and low number of labeled training samples pose the setback of the curse of dimensionality and, accordingly, result in the possibility of overfitting the training data. With the aim of solving all these complications, in this study presented the framework of Support Vector Machine (SVM) together with Fuzzy Sigmoid Kernel Function (SVM-FSK) in the circumstance of HSI classification and analyzing their features in the hyperspectral domain. A Kernel Fisher Discriminant Analysis (KFDA) model is employed for the purpose of dimensionality reduction of HSI. The KFDA dimensionality reduction scheme depends on the selection of the kernel in a higher-dimensional HSI feature space. In order to enhance the gradient level of spatial information, employed Improved Empirical Mode Decomposition (IEMD) with Gaussian Firefly Algorithm (GFA) (IEMD-GFA) to boost the mixed pixel wise SVM-FSK classification accuracy. During the process of IEMD scheme, the identifiable of Intrinsic Mode Functions (IMFs) of spectral band, weight values of IMFs are computed with the help of GFA. In order to identify the shape of HSI, novel hybrid scheme depending on the canny operator and fuzzy entropy theory is formulated. This scheme computes the fuzzy entropy of gradients from an image to make a decision on the threshold for the canny operator. For the purpose of detecting the edges and to discover the shape of the object Weibull Probability Density Function (WPDF) scheme is used. The obtained both spectral and spatial pixels are classified using SVM-FSK and estimated by using Hierarchical Dirichlet Process (HDP)-Hidden Markov Model (HMM). The proposed SVM-FSK is assessed with hyperspectral AVIRIS Indian Pine dataset. It shows that the proposed dimensionality reduction with SVM-FSK classification shows improved classification accuracy in terms of parameters like overall accuracy, standard deviation and mean.

Macroweather precipitation variability up to global and centennial scales

AbstractWe investigate precipitation variability in the “macroweather” regime—the intermediate regime between the familiar weather and climate regimes—which is associated to time scales from about 10 days to 30–100 years. Macroweather precipitation is characterized by negative fluctuation exponents. This implies—contrary to the weather regime—that fluctuations tend to cancel each other out, they diminish with time scale, this is important for seasonal, annual, and decadal forecasts. Aiming at a wide‐scale range space‐time statistical description of macroweather precipitation, we study the scaling of three centennial, global‐scale precipitation products (one gauge based, one reanalysis based, and one satellite based) and systematically compare them over wide ranges of time and space scales. Although these products have very similar temporal statistics, at 5° resolution, they only agree with each other after being averaged over scales of several years, at scales larger than 2–3 decades, they disagree again. In space, there is less agreement on the statistics but—since the data have low resolutions (mostly 5° × 5°)—the disagreement is only over a small overall range of scales: the monthly data agree fairly well at scales 20°–30° and larger. Moreover, we quantify the outer scale limit of the temporal scaling (20–40 years, depending on the product, on the spatial scale, pixel, or global). Overall, results show that precipitation can be modeled with space‐time scaling processes. The improved understanding of the space‐time macroweather precipitation variability and the limitations of precipitation products provided by this work opens new perspectives to the stochastic modeling and forecasting of macroweather precipitation as well as separating natural and anthropogenic precipitation.

Understanding the timing of economic feasibility: The case of input interfaces for human-computer interaction

The objective of this paper is to understand when new types of input interfaces for Human–Computer Interaction (HCI) such as Natural User Interfaces (NUI) (e.g., speech and gesture) and Direct Neural Interfaces (DNI), or combinations of them, might become technologically and economically feasible. This problem is addressed by analyzing the performance trajectories of key components in these HCI systems. In the case of speech interfaces, we observe that microphones and automated speech recognition systems are no longer experiencing rapid improvements along key dimensions of performance, which inhibits their technical and economic feasibility. On the other hand, 2D image sensors and depth sensors, which constitute the core components of gesture interfaces, are continuing to improve at a significant rate in terms of characteristics like spatial resolution, pixel sensitivity, and depth resolution. When coupled with the exponential improvements in the memory and processing power of computing systems, the above improvements in image sensors are enabling gesture-based natural user interfaces to reach acceptable levels of technical performance and economic feasibility. Similarly, simultaneous improvement in the spatial and temporal resolution of non-invasive brain scanning technologies is likely to accelerate the development of direct neural interfaces (DNI). However, a number of challenging obstacles such as lack of robust magnetic shielding systems, high cost, and poor usability continue to hinder the economic feasibility of DNI systems.

On-Off Keying Communication Over Optical Channels With Crosstalk

We investigate the fundamental limits of communication over optical on-off-keying channels with crosstalk, where a light pulse may span over multiple time slots or spatial pixels, and the receiver is equipped with single-photon detectors. First, we analyze achievable rates of communication over such channels, and observe that increasing transmission power (expected number of photons emitted per slot or pixel) does not necessarily lead to higher rates. Under simple but reasonable models, the highest rates are often achieved in a low-photon regime, with an average of 3 to 7 photons received in each slot or pixel. We further characterize the tradeoff between information rate and photon efficiency (in terms of the expected number of bits transmitted per photon) in the presence of crosstalk. Finally, we develop guidelines for slot length and pixel size selection for different application scenarios. Our analysis reveals that optimum optical-communication systems do not minimize the level of crosstalk.

Infrared sensor signal generation method for direct signal injection simulation

Direct signal injection (DSI) simulation has been widely used in the performance testing and evaluation of an infrared imaging system (IRIS). A method to generate a high realism infrared sensor signal (HRIRSS) for DSI simulation is proposed. The IR sensor signal generation system hardware is based on a computer and field-programmable gate array processor. Mathematical models are adopted to quantitatively characterize the imaging physical effects (IPES) of the IRIS. Spatial template convolution and pixel processing approaches are used to realize high-speed real-time computation of the IPES. Taking advantages from low-noise circuit design methodologies, we designed a low-noise digital–analog converter to convert digital images generated by the system to analog signals for DSI simulation. The noise voltage of the converter within the passband is less than 244 μV, which ensures the system a 14-bit precision. For simulation of IR sensors with a resolution of 320×256 pixels, the processing speed is up to 240 frames per second (fps) and that for the 128×128 pixel sensor is 480 fps. The test results also show that the generated IR sensor signals have high confidence. In addition, the system has the advantages of simple structure, strong expansibility, low cost, and it has been used in practical applications.

Classification of Hyperspectral Images by Exploiting Spectral–Spatial Information of Superpixel via Multiple Kernels

For the classification of hyperspectral images (HSIs), this paper presents a novel framework to effectively utilize the spectral–spatial information of superpixels via multiple kernels, which is termed as superpixel-based classification via multiple kernels (SC-MK). In the HSI, each superpixel can be regarded as a shape-adaptive region, which consists of a number of spatial neighboring pixels with very similar spectral characteristics. First, the proposed SC-MK method adopts an oversegmentation algorithm to cluster the HSI into many superpixels. Then, three kernels are separately employed for the utilization of the spectral information, as well as spatial information, within and among superpixels. Finally, the three kernels are combined together and incorporated into a support vector machine classifier. Experimental results on three widely used real HSIs indicate that the proposed SC-MK approach outperforms several well-known classification methods.

Noise performance of the multiwavelength sub/millimeter inductance camera (MUSIC) detectors

MUSIC is a multi-band imaging camera that employs 2304 Microwave Kinetic Inductance Detectors (MKIDs) in 576 spatial pixels to cover a 14 arc-minute field of view, with each pixel simultaneously sensitive to 4 bands centered at 0.87, 1.04, 1.33, and 1.98 mm. In April 2012 the MUSIC instrument was commissioned at the Caltech Submillimeter Observatory with a subset of the full focal plane. We examine the noise present in the detector timestreams during observations taken in the first year of operation. We find that fluctuations in atmospheric emission dominate at long timescales (< 0.5 Hz), and fluctuations in the amplitude and phase of the probe signal due to readout electronics contribute significant 1/f-type noise at shorter timescales. We describe a method to remove the amplitude, phase, and atmospheric noise using the fact that they are correlated among carrier tones. After removal, the complex signal is decomposed, or projected, into dissipation and frequency components. White noise from the cryogenic HEMT amplifier dominates in the dissipation component. An excess noise is observed in the frequency component that is likely due to fluctuations in two-level system (TLS) defects in the device substrate. We compare the amplitude of the TLS noise with previous measurements.

P‐98: A New Blue‐Pixel Design for Improving the Side View Performance

We propose a new kind of low color shift design (LCS). With normal pixel structure, LCD panel can keep transmittance and with spatial blue pixels signal conversion it can improve side view performance and washout phenomenon. Results and details of such spatial LCS have been observed and evaluated.