Clean Map Research Articles

Classification of Space Micromotion Targets With Similar Shapes at Low SNR

There are a number of targets in space that are similar in shape, but they pose different threat levels. It is necessary to discriminate these targets for the sake of space security. The unique characteristics of detailed micro-Doppler (MD) signatures can be used to classify micromotion forms, such as spinning, precession, nutation, wobbling, and tumbling. In this letter, we design a novel processing flow for classifying five types of micromotion under low signal-to-noise ratio (SNR) condition. We start from the range map and successively perform noise-level estimation, adaptive filtering, and multithreshold segmentation to obtain an ideal binary mask. The clean range maps are used as the final input to an eight-layer convolutional neural network (CNN) for training, validation, and testing. The experiment results show that our method achieves more than 80% classification accuracy for micromotion in the case of −10 dB, which is higher than the existing popular methods.

An Application of the Global ILC Algorithm over Large Angular Scales to Estimate the CMB Posterior Using Gibbs Sampling

Abstract In this work, we formalize a new technique to investigate the joint posterior density of the cosmic microwave background (CMB) signal and its theoretical angular power spectrum given the observed data, using the global internal-linear-combination method first proposed in a paper by Sudevan & Saha in 2017. We implement the method on low-resolution CMB maps observed by the Wilkinson Microwave Anisotropy Probe (WMAP) and the Planck satellite missions, using Gibbs sampling, assuming that the detector noise is negligible on large angular scales of the sky. The main products of our analysis are a best-fit cleaned CMB map and its theoretical angular power spectrum, along with their error estimates. We validate the method by performing Monte Carlo simulations that include realistic foreground models and noise levels consistent with WMAP and Planck observations. Our method has a unique advantage: the posterior density is obtained without any need to explicitly model foreground components. Second, the power spectrum results with the error estimates can be directly used for cosmological parameter estimations.

PACO ASDI: an algorithm for exoplanet detection and characterization in direct imaging with integral field spectrographs

Context.Exoplanet detection and characterization by direct imaging both rely on sophisticated instruments (adaptive optics and coronagraph) and adequate data processing methods. Angular and spectral differential imaging (ASDI) combines observations at different times and a range of wavelengths in order to separate the residual signal from the host star and the signal of interest corresponding to off-axis sources.Aims.Very high contrast detection is only possible with an accurate modeling of those two components, in particular of the background due to stellar leakages of the host star masked out by the coronagraph. Beyond the detection of point-like sources in the field of view, it is also essential to characterize the detection in terms of statistical significance and astrometry and to estimate the source spectrum.Methods.We extend our recent methodPACO, based on local learning of patch covariances, in order to capture the spectral and temporal fluctuations of background structures. From this statistical modeling, we build a detection algorithm and a spectrum estimation method:PACO ASDI. The modeling of spectral correlations proves useful both in reducing detection artifacts and obtaining accurate statistical guarantees (detection thresholds and photometry confidence intervals).Results.An analysis of several ASDI datasets from the VLT/SPHERE-IFS instrument shows thatPACO ASDIproduces very clean detection maps, for which setting a detection threshold is statistically reliable. Compared to other algorithms used routinely to exploit the scientific results of SPHERE-IFS, sensitivity is improved and many false detections can be avoided. Spectrally smoothed spectra are also produced byPACO ASDI. The analysis of datasets with injected fake planets validates the recovered spectra and the computed confidence intervals.Conclusions.PACO ASDIis a high-contrast processing algorithm accounting for the spatio-spectral correlations of the data to produce statistically-grounded detection maps and reliable spectral estimations. Point source detections, photometric and astrometric characterizations are fully automatized.

Improving the resolution of underwater acoustic image measurement by deconvolution

The conventional beamforming (CBF) map of underwater acoustic image measurement can be expressed as a convolution of the source distribution and the response of the beamformer to a point source, defined as the beam pattern or the point spread function (PSF). By deconvolving the CBF beamformer’s map, the distribution of the actual sources with a clean beam map can be obtained to improve the resolution. In this paper, the deconvolved conventional beamforming (dCv) is applied to underwater acoustic image measurement to improve the accuracy of sound source localization. Due to the point spread function of the array in near field is dependent on the focused point, which means that the PSF is shift variant, so it is necessary to use shift-variant deconvolution method. In this paper, an extended Richardson-Lucy (Ex-RL) algorithm is applied to the two-dimensional shift-variant deconvolution acoustic image measurement problem to improve resolution. The method is compared with the original-RL, classical deconvolution approach for the mapping of acoustic sources (DAMAS) and non-negative least squares (NNLS). The simulation and the experiment results verify the effectiveness of the algorithm.

A hybrid data mining heuristic to solve the point‐feature cartographic label placement problem

AbstractA clean map visualization requires the fewest possible overlaps and depends on how labels are attached to point features. In this paper, we address the cartographic label placement variant problem whose objective is to label a set of points maximizing the number of conflict‐free points. Thus, we propose a hybrid data mining heuristic to solve the point‐feature cartographic label placement problem based on a clustering search (CS) heuristic, a state‐of‐the‐art method for this problem. Although several works have investigated the combination of data mining and multistart metaheuristics, this is the first time data mining has been used to improve CS and simulated annealing based heuristics. Computational experiments showed that the proposed hybrid heuristic was able to reach better cost solutions than the original strategy, with the same time effort. The proposed heuristic also could find almost all known optimal solutions and improved most of the best results for the set of large instances reported so far in the literature.

Data driven foreground clustering approach to component separation in multifrequency CMB experiments: a new Planck CMB map

We present a new approach to component separation in multifrequency CMB experiments by formulating the problem as that of partitioning the sky into pixel clusters such that within each pixel cluster the foregrounds have similar spectrum, using only the information available in the data. Only spectral information is used for partitioning, allowing spatially far away pixels to belong to the same cluster if their foreground properties are close. We then apply a modified internal linear combination method to each pixel cluster. Since the foregrounds have similar spectrum within each cluster, the number of components required to describe the foregrounds is smaller compared to all data taken together and simple pixel based ILC algorithm works extremely well. We test our algorithm in the full focal plane simulations provided by the Planck collaboration. We apply our algorithm to the Planck full mission data and compare our CMB maps with the CMB maps released by the Planck collaboration. We show that our CMB maps are clean and unbiased on a larger fraction of the sky, especially at the low Galactic latitudes, compared to publicly available maps released by the Planck collaboration. This is important for constraining beyond the simplest ΛCDM cosmological models and study of anomalies. Our cleaned CMB maps are made publicly available for use by the cosmology community.

Protocol for automating error removal from yield maps

Yield mapping is one of the most widely used precision farming technologies. However, the value of the maps can be compromised by the presence of systematic and random errors in raw within field data. In this paper, an automated method to clean yield maps is proposed so as to ensure the quality of further data processing and management decisions. First, data were screened by filtering null and edge yield values as well global outliers. Second, spatial outliers or local defective observations were deleted. The local Moran’s index of spatial autocorrelation and Moran’s plot were used as tool to identify the spatial outliers. The protocol to filter out global and local outliers was evaluated on 595 real yield datasets from different grain crops. Significant improvements in the distribution and spatial structure of yield datasets was found. Approximately 30% of the dataset size was removed from each monitor dataset, with one third of the removal occurring during filtering of spatial outliers. The automation of null, edge yield values and the removal of global outliers improved yield distributions, whereas the cleaning of local outliers impacted the yield spatial structure for all yield maps and crops. The algorithm proposed to clean yield maps is easy to apply for preprocessing the growing number of available yield maps.

Salient Object Detection via Fuzzy Theory and Object-Level Enhancement

This paper proposes a bottom-up saliency detection method via effective integration of regional saliency measure and object-level information using fuzzy theory. First, we generate an initial saliency map by fusing multiple prior maps. Second, to emphasize the object-level concept of saliency, we further generate many object proposals of the input image. A fuzzy set theory is then applied to measure the objectness score of the object proposals and integrate them into an objectness map. Third, an optimization framework is proposed to effectively fuse various prior saliency cues and object-level information to produce a clean and uniform saliency map as well as to maintain the salient object completeness. Experimental studies in several benchmark datasets confirmed the superiority of the proposed method over state-of-the-art saliency detection methods.

A Global ILC Approach in Pixel Space over Large Angular Scales of the Sky Using CMB Covariance Matrix

Abstract We propose a new internal linear combination (ILC) method in the pixel space, applicable on large angular scales of the sky, to estimate a foreground-minimized cosmic microwave background (CMB) temperature anisotropy map by incorporating prior knowledge about the theoretical CMB covariance matrix. The usual ILC method in pixel space, on the contrary, does not use any information about the underlying CMB covariance matrix. The new approach complements the usual pixel space ILC technique specifically at low-multipole regions, using global information available from the theoretical CMB covariance matrix and from the data. Since we apply our method over the large scale on the sky containing low multipoles, we perform foreground minimization globally. We apply our methods on low-resolution Planck and WMAP foreground-contaminated CMB maps and validate the methodology by performing detailed Monte Carlo simulations. Our cleaned CMB map and its power spectrum have significantly less error than those obtained following the usual ILC technique at low resolution that does not use CMB covariance information. Another very important advantage of our method is that the cleaned power spectrum does not have any negative bias at the low multipoles because of effective suppression of CMB–foreground chance correlations on large angular scales of the sky. Our cleaned CMB map and its power spectrum match well with those estimated by other research groups.

Exploring cosmic origins with CORE: Gravitational lensing of the CMB

Lensing of the cosmic microwave background (CMB) is now a well-developed probe of the clustering of the large-scale mass distribution over a broad range of redshifts. By exploiting the non-Gaussian imprints of lensing in the polarization of the CMB, the CORE mission will allow production of a clean map of the lensing deflections over nearly the full-sky. The number of high-S/N modes in this map will exceed current CMB lensing maps by a factor of 40, and the measurement will be sample-variance limited on all scales where linear theory is valid. Here, we summarise this mission product and discuss the science that will follow from its power spectrum and the cross-correlation with other clustering data. For example, the summed mass of neutrinos will be determined to an accuracy of 17 meV combining CORE lensing and CMB two-point information with contemporaneous measurements of the baryon acoustic oscillation feature in the clustering of galaxies, three times smaller than the minimum total mass allowed by neutrino oscillation measurements. Lensing has applications across many other science goals of CORE, including the search for B-mode polarization from primordial gravitational waves. Here, lens-induced B-modes will dominate over instrument noise, limiting constraints on the power spectrum amplitude of primordial gravitational waves. With lensing reconstructed by CORE, one can “delens” the observed polarization internally, reducing the lensing B-mode power by 60 %. This can be improved to 70 % by combining lensing and measurements of the cosmic infrared background from CORE, leading to an improvement of a factor of 2.5 in the error on the amplitude of primordial gravitational waves compared to no delensing (in the null hypothesis of no primordial B-modes). Lensing measurements from CORE will allow calibration of the halo masses of the tens of thousands of galaxy clusters that it will find, with constraints dominated by the clean polarization-based estimators. The 19 frequency channels proposed for CORE will allow accurate removal of Galactic emission from CMB maps. We present initial findings that show that residual Galactic foreground contamination will not be a significant source of bias for lensing power spectrum measurements with CORE.

Improved Diffuse Foreground Subtraction with the ILC Method: CMB Map and Angular Power Spectrum Using Planck and WMAP Observations

Abstract We report an improved technique for diffuse foreground minimization from Cosmic Microwave Background (CMB) maps using a new multiphase iterative harmonic space internal-linear-combination (HILC) approach. Our method nullifies a foreground leakage that was present in the old and usual iterative HILC method. In phase 1 of the multiphase technique, we obtain an initial cleaned map using the single iteration HILC approach over the desired portion of the sky. In phase 2, we obtain a final CMB map using the iterative HILC approach; however, now, to nullify the leakage, during each iteration, some of the regions of the sky that are not being cleaned in the current iteration are replaced by the corresponding cleaned portions of the phase 1 map. We bring all input frequency maps to a common and maximum possible beam and pixel resolution at the beginning of the analysis, which significantly reduces data redundancy, memory usage, and computational cost, and avoids, during the HILC weight calculation, the deconvolution of partial sky harmonic coefficients by the azimuthally symmetric beam and pixel window functions, which in a strict mathematical sense, are not well defined. Using WMAP 9 year and Planck 2015 frequency maps, we obtain foreground-cleaned CMB maps and a CMB angular power spectrum for the multipole range . Our power spectrum matches the published Planck results with some differences at different multipole ranges. We validate our method by performing Monte Carlo simulations. Finally, we show that the weights for HILC foreground minimization have the intrinsic characteristic that they also tend to produce a statistically isotropic CMB map.

Salient object detection via multiple saliency weights

Salient object detection aims to emulate the extraordinary capability of human visual system, which has the ability to find the most visually attractive objects in a complex visual scene. The human visual attention is often complicated and affected by many factors. In this paper, we present a novel bottom-up approach to automatically detect salient objects of an image via multiple visual cues. The key idea of our approach is to represent a saliency map of an image as an integration of multiple visual cues (saliency weights), which have been proven to be effective and useful. Specifically, we propose four saliency weights, i.e., local contrast weight, superpixel clarity weight, background probability weight, and central bias weight, to effectively represent each visual cue. To obtain our saliency map, the four resulting saliency weights are integrated in a principled way via multiplication and summation based fusion. Furthermore, we propose a new superpixel-level saliency smoothing approach to optimize the integrated results for producing clean and consistent saliency maps. Our experimental results on three standard benchmark datasets demonstrate that the proposed approach outperforms other saliency detection approaches in terms of the subjective observations and objective evaluations.

Large-region acoustic source mapping using a movable array and sparse covariance fitting.

Large-region acoustic source mapping is important for city-scale noise monitoring. Approaches using a single-position measurement scheme to scan large regions using small arrays cannot provide clean acoustic source maps, while deploying large arrays spanning the entire region of interest is prohibitively expensive. A multiple-position measurement scheme is applied to scan large regions at multiple spatial positions using a movable array of small size. Based on the multiple-position measurement scheme, a sparse-constrained multiple-position vectorized covariance matrix fitting approach is presented. In the proposed approach, the overall sample covariance matrix of the incoherent virtual array is first estimated using the multiple-position array data and then vectorized using the Khatri-Rao (KR) product. A linear model is then constructed for fitting the vectorized covariance matrix and a sparse-constrained reconstruction algorithm is proposed for recovering source powers from the model. The user parameter settings are discussed. The proposed approach is tested on a 30 m × 40 m region and a 60 m × 40 m region using simulated and measured data. Much cleaner acoustic source maps and lower sound pressure level errors are obtained compared to the beamforming approaches and the previous sparse approach [Zhao, Tuna, Nguyen, and Jones, Proc. IEEE Intl. Conf. on Acoustics, Speech and Signal Processing (ICASSP) (2016)].

An imaging algorithm for damage detection with dispersion compensation using piezoceramic induced lamb waves

Piezoceramic induced Lamb waves are often used for imaging based damage detection, especially for plate like structures. The dispersion effect of the Lamb waves deteriorates the performance of most of imaging methods, since the waveform of the dispersion signals will spread out. In this paper, an imaging method which can compensate the dispersion is developed. In the proposed method, the phase induced by the propagation distance is compensated firstly. After that, the phase deviation generated by the dispersion effect is compensated. Via the two compensations, the proposed method can derive an accurate location of the target with a clean imaging map. An experiment using a plate like structure with four piezoceramic transducer was conducted. In the experiment, the four piezoceramic sensors were used to obtain the signals of the scatterer that simulated the damage on an aluminum plate. The experimental results show that since the dispersion effect is compensated, the target’s image based on the proposed method is about 10 cm × 14 cm, which is about a quarter of that of using the back-projection imaging method.

Combining background information and a top-down model for computing salient objects

Predicting the salient object region in real scenes has progressed significantly in recent years. In this work, we propose a novel method for computing salient object regions by combining background information and a top-down visual saliency model, which is well-suited for locating category-specific salient objects in cluttered real scenes. First, we used a robust background measure to acquire clean saliency maps by optimizing background information. Second, we learned a top-down saliency object model by combining a class-specific codebook and conditional random fields (CRFs) during the training phase. Furthermore, our model used the locality-constrained linear codes as latent CRF variables. Finally, we computed salient object regions by combining the robust background measure and top-down model. Experimental results on the Graz-02 and PASCAL VOC2007 datasets show that our method creates much better saliency maps than current state-of-the-art methods.

High resolution VLBI polarization imaging of AGN with the maximum entropy method

Radio polarisation images of the jets of Active Galactic Nuclei (AGN) can provide a deep insight into the launching and collimation mechanisms of relativistic jets. However, even at VLBI scales, resolution is often a limiting factor in the conclusions that can be drawn from observations. The Maximum Entropy Method (MEM) is a deconvolution algorithm that can outperform the more common CLEAN algorithm in many cases, particularly when investigating structures present on scales comparable to or smaller than the nominal beam size with "super-resolution". A new implementation of the MEM suitable for single- or multiple-wavelength VLBI polarisation observations has been developed and is described here. Monte Carlo simulations comparing the performances of CLEAN and MEM at reconstructing the properties of model images are presented; these demonstrate the enhanced reliability of MEM over CLEAN when images of the fractional polarisation and polarisation angle are constructed using convolving beams that are appreciably smaller than the full CLEAN beam. The results of using this new MEM software to image VLBA observations of the AGN 0716+714 at six different wavelengths are presented, and compared to corresponding maps obtained with CLEAN. MEM and CLEAN maps of Stokes $I$, the polarised flux, the fractional polarisation and the polarisation angle are compared for convolving beams ranging from the full CLEAN beam down to a beam one-third of this size. MEM's ability to provide more trustworthy polarisation imaging than a standard CLEAN-based deconvolution when convolving beams appreciably smaller than the full CLEAN beam are used is discussed.

F(R)-gravity model of the Sunyaev-Zeldovich profile of the Coma cluster compatible with Planck data

In the weak field limit, analytic $f(R)$ models of gravity introduce a Yukawa-like correction to the Newtonian gravitational potential. These models have been widely tested at galactic scales and provide an alternative explanation to the dynamics of galaxies without Dark Matter. We study if the temperature anisotropies due to the thermal Sunyaev-Zeldovich effect are compatible with these Extended Theories of Gravity. We assume that the gas is in hydrostatic equilibrium within the modified Newtonian potential and it is well described by a polytropic equation of state. We particularize the model for the Coma cluster and the predicted anisotropies are compared with those measured in the foreground cleaned maps obtained using the Planck Nominal maps released in 2013. We show that the computed $f(R)$ pressure profile fits the data giving rise to competitive constraints of the Yukawa scale length $L=(2.19\pm1.02) \rm{\, Mpc}$, and of the deviation parameter $ \delta=-0.48\pm0.22$. Those are currently the tightest constraints at galaxy cluster scale, and support the idea that Extended Theories of Gravity provide an alternative explanation to the dynamics of self-gravitating systems without requiring Dark Matter.

SILC: a newPlanckinternal linear combination CMB temperature map using directional wavelets

We present new clean maps of the CMB temperature anisotropies (as measured by Planck) constructed with a novel internal linear combination (ILC) algorithm using directional, scale-discretised wavelets --- Scale-discretised, directional wavelet ILC or SILC. Directional wavelets, when convolved with signals on the sphere, can separate the anisotropic filamentary structures which are characteristic of both the CMB and foregrounds. Extending previous component separation methods, which use the frequency, spatial and harmonic signatures of foregrounds to separate them from the cosmological background signal, SILC can additionally use morphological information in the foregrounds and CMB to better localise the cleaning algorithm. We test the method on Planck data and simulations, demonstrating consistency with existing component separation algorithms, and discuss how to optimise the use of morphological information by varying the number of directional wavelets as a function of spatial scale. We find that combining the use of directional and axisymmetric wavelets depending on scale could yield higher quality CMB temperature maps. Our results set the stage for the application of SILC to polarisation anisotropies through an extension to spin wavelets.

Cross-correlation analysis of CMB with foregrounds for residuals

In this paper, we try to probe whether a clean CMB map obtained from the raw satellite data using a cleaning procedure is sufficiently clean. Specifically we study if there are any foreground residuals still present in the cleaned data using a cross-correlation statistic. Residual contamination is expected to be present, primarily, in the galactic plane due to the high emission from our own galaxy. A foreground mask is applied conventionally to avoid biases in the estimated quantities of interest due to foreground leakage. Here, we map foreground residuals, if present, in the unmasked region i.e., outside a CMB analysis mask. Further locally extended foreground-contaminated regions, found eventually, are studied to understand them better. The few contaminated regions thus identified may be used to slightly extend the available masks to make them more stringent.

A structure-based region detector for high-resolution retinal fundus image registration

Abstract A fundamental problem of retinal fundus image registration is the determination of corresponding points. The scale-invariant feature transform (SIFT) is a well-known algorithm in this regard. However, SIFT suffers from the problems in the quantity and quality of the detected points when facing with high-resolution and low-contrast retinal fundus images. On the other hand, the attention of human visual systems directs to regions instead of points for feature matching. Being aware of these issues, this paper presents a new structure-based region detector, which identifies stable and distinctive regions, to find correspondences. Meanwhile, it describes a robust retinal fundus image registration framework. The region detector is based on a robust watershed segmentation that obtains closed-boundary regions within a clean vascular structure map. Since vascular structure maps are relatively stable in partially overlapping and temporal image pairs, the regions are unaffected by viewpoint, content and illumination variations of retinal images. The regions are approximated by convex polygons, so that robust boundary descriptors are achieved to match them. Finally, correspondences determine the parameters of geometric transformation between input images. Experimental results on four datasets including temporal and partially overlapping image pairs show that our approach is comparable or superior to SIFT-based methods in terms of efficiency, accuracy and speed. The proposed method successfully registered 92.30% of 130 temporal image pairs and 91.42% of 70 different field of view image pairs.