Showing papers on "Subpixel rendering published in 2015"

A Novel Subpixel Phase Correlation Method Using Singular Value Decomposition and Unified Random Sample Consensus

Abstract: Subpixel translation estimation using phase correlation is a fundamental task for numerous applications in the remote sensing community. The major drawback of the existing subpixel phase correlation methods lies in their sensitivity to corruption, including aliasing and noise, as well as the poor performance in the case of practical remote sensing data. This paper presents a novel subpixel phase correlation method using singular value decomposition (SVD) and the unified random sample consensus (RANSAC) algorithm. In the proposed method, SVD theoretically converts the translation estimation problem to one dimensions for simplicity and efficiency, and the unified RANSAC algorithm acts as a robust estimator for the line fitting, in this case for the high accuracy, stability, and robustness. The proposed method integrates the advantages of Hoge's method and the RANSAC algorithm and avoids the corresponding shortfalls of the original phase correlation method based only on SVD. A pixel-to-pixel dense matching scheme on the basis of the proposed method is also developed for practical image registration. Experiments with both simulated and real data were carried out to test the proposed method. In the simulated case, the comparative results estimated from the generated synthetic image pairs indicate that the proposed method outperforms the other existing methods in the presence of both aliasing and noise, in both accuracy and robustness. Moreover, the pixel locking effect that commonly occurs in subpixel matching was also investigated. The degree of pixel locking effect was found to be significantly weakened by the proposed method, as compared with the original Hoge's method. In the real data case, experiments using different bands of ZY-3 multispectral sensor-corrected images demonstrate the promising performance and feasibility of the proposed method, which is able to identify seams of the image stitching between sub-charge-coupled device units.

An Adaptive Subpixel Mapping Method Based on MAP Model and Class Determination Strategy for Hyperspectral Remote Sensing Imagery

Abstract: The subpixel mapping technique can specify the spatial distribution of different categories at the subpixel scale by converting the abundance map into a higher resolution image, based on the assumption of spatial dependence. Traditional subpixel mapping algorithms only utilize the low-resolution image obtained by the classification image downsampling and do not consider the spectral unmixing error, which is difficult to account for in real applications. In this paper, to improve the accuracy of the subpixel mapping, an adaptive subpixel mapping method based on a maximum a posteriori (MAP) model and a winner-take-all class determination strategy, namely, AMCDSM, is proposed for hyperspectral remote sensing imagery. In AMCDSM, to better simulate a real remote sensing scene, the low-resolution abundance images are obtained by the spectral unmixing method from the downsampled original image or real low-resolution images. The MAP model is extended by considering the spatial prior models (Laplacian, total variation (TV), and bilateral TV) to obtain the high-resolution subpixel distribution map. To avoid the setting of the regularization parameter, an adaptive parameter selection method is designed to acquire the optimal subpixel mapping results. In addition, in AMCDSM, to take into account the spectral unmixing error in real applications, a winner-take-all strategy is proposed to achieve a better subpixel mapping result. The proposed method was tested on simulated, synthetic, and real hyperspectral images, and the experimental results demonstrate that the AMCDSM algorithm outperforms the traditional subpixel mapping methods and provides a simple and efficient algorithm to regularize the ill-posed subpixel mapping problem.

SAR Image Registration Using Phase Congruency and Nonlinear Diffusion-Based SIFT

Abstract: The scale-invariant feature transform (SIFT) algorithm has been widely applied to optical image registration. However, mostly because of multiplicative speckle noise, SIFT has a limited performance when directly applied to synthetic aperture radar (SAR) image. In this letter, a novel SAR image registration method is proposed, which is based on the combination of SIFT, nonlinear diffusion, and phase congruency. In our proposed algorithm, the multiscale representation of a SAR image is generated by nonlinear diffusion, since it better preserves edges in the image as opposed to Gaussian smoothing, which is used in the original SIFT. To reduce the influence of multiplicative speckle noise, the ratio of exponential weighted average operator is used to compute the gradient information in the construction of nonlinear diffusion scale space. Moreover, phase congruency information is utilized to remove the erroneous keypoints within the initial keypoints. Experimental results on multipolarization, multiband, and multitemporal SAR images indicate that our algorithm can improve the match performance compared to the SIFT-based method, which leads to a subpixel accuracy for all the tested image pairs.

Fourier-based interpolation bias prediction in digital image correlation.

Abstract: Based on the Fourier method, this paper deduces analytic formulae for interpolation bias in digital image correlation, explains the well-known sinusoidal-shaped curves of interpolation bias, and introduces the concept of interpolation bias kernel, which characterizes the frequency response of the interpolation bias and thus provides a measure of the subset matching quality of the interpolation algorithm. The interpolation bias kernel attributes the interpolation bias to aliasing effect of interpolation and indicates that high-frequency components are the major source of interpolation bias. Based on our theoretical results, a simple and effective interpolation bias prediction approach, which exploits the speckle spectrum and the interpolation transfer function, is proposed. Significant acceleration is attained, the effect of subset size is analyzed, and both numerical simulations and experimental results are found to agree with theoretical predictions. During the experiment, a novel experimental translation technique was developed that implements subpixel translation of a captured image through integer pixel translation on a computer screen. Owing to this remarkable technique, the influences of mechanical error and out-of-plane motion are eliminated, and complete interpolation bias curves as accurate as 0.01 pixel are attained by subpixel translation experiments.

Land Cover Change Detection at Subpixel Resolution With a Hopfield Neural Network

Abstract: In this paper, a new subpixel resolution land cover change detection (LCCD) method based on the Hopfield neural network (HNN) is proposed. The new method borrows information from a known fine spatial resolution land cover map (FSRM) representing one date for subpixel mapping (SPM) from a coarse spatial resolution image on another, closer date. It is implemented by using the thematic information in the FSRM to modify the initialization of neuron values in the original HNN. The predicted SPM result was compared to the original FSRM to achieve subpixel resolution LCCD. The proposed method was compared with the original unmodified HNN method as well as six state-of-the-art methods for LCCD. To explore the effect of uncertainty in spectral unmixing, which mainly originates from spectral separability in the input, coarse image, and the point spread function (PSF) of the sensor, a set of synthetic multispectral images with different class separabilities and PSFs was used in experiments. It was found that the proposed LCCD method (i.e., HNN with an FSRM) can separate more real changes from noise and produce more accurate LCCD results than the state-of-the-art methods. The advantage of the proposed method is more evident when the class separability is small and the variance in the PSF is large, that is, the uncertainty in spectral unmixing is large. Furthermore, the utilization of an FSRM can expedite the HNN-based processing required for LCCD. The advantage of the proposed method was also validated by applying to a set of real Landsat-Moderate Resolution Imaging Spectroradiometer (MODIS) images.

Indicator Cokriging-Based Subpixel Mapping Without Prior Spatial Structure Information

Abstract: Indicator cokriging (ICK) has been shown to be an effective subpixel mapping (SPM) algorithm. It is noniterative and involves few parameters. The original ICK-based SPM method, however, requires the semivariogram of land cover classes from prior information, usually in the form of fine spatial resolution training images. In reality, training images are not always available, or laborious work is needed to acquire them. This paper aims to seek spatial structure information for ICK when such prior land cover information is not obtainable. Specifically, the fine spatial resolution semivariogram of each class is estimated by the deconvolution process, taking the coarse spatial resolution semivariogram extracted from the class proportion image as input. The obtained fine spatial resolution semivariogram is then used to estimate class occurrence probability at each subpixel with the ICK method. Experiments demonstrated the feasibility of the proposed ICK with the deconvolution approach. It obtains comparable SPM accuracy to ICK that requires semivariogram estimated from fine spatial resolution training images. The proposed method extends ICK to cases where the prior spatial structure information is unavailable.

Fast Subpixel Mapping Algorithms for Subpixel Resolution Change Detection

Abstract: Due to rapid changes on the Earth's surface, it is important to perform land cover change detection (CD) at a fine spatial and fine temporal resolution. However, remote sensing images with both fine spatial and temporal resolutions are commonly not available or, where available, may be expensive to obtain. This paper attempts to achieve fine spatial and temporal resolution land cover CD with a new computer technology based on subpixel mapping (SPM): The fine spatial resolution land cover maps (FRMs) are first predicted through SPM of the coarse spatial but fine temporal resolution images, and then, subpixel resolution CD is performed by comparison of class labels in the SPM results. For the first time, five fast SPM algorithms, including bilinear interpolation, bicubic interpolation, subpixel/pixel spatial attraction model, Kriging, and radial basis function interpolation methods, are proposed for subpixel resolution CD. The auxiliary information from the known FRM on one date is incorporated in SPM of coarse images on other dates to increase the CD accuracy. Based on the five fast SPM algorithms and the availability of the FRM, subpixels for each class are predicted by comparison of the estimated soft class values at the target fine spatial resolution and borrowing information from the FRM. Experiments demonstrate the feasibility of the five SPM algorithms using FRM in subpixel resolution CD. They are fast methods to achieve subpixel resolution CD.

Hybrid Constraints of Pure and Mixed Pixels for Soft-Then-Hard Super-Resolution Mapping With Multiple Shifted Images

Abstract: Multiple shifted images (MSIs) have been widely applied to many super-resolution mapping (SRM) approaches to improve the accuracy of fine-scale land-cover maps. Most SRM methods with MSIs involve two processes: subpixel sharpening and class allocation. Complementary information from the MSIs has been successfully adopted to produce soft attribute values of subpixels during the subpixel sharpening process. Such information, however, is not used in the second process of class allocation. In this paper, a new class-allocation algorithm, named “hybrid constraints of pure and mixed pixels” (HCPMP), is proposed to allocate land-cover classes to subpixels using MSIs. HCPMP first determines the classes of subpixels that overlap with the pure pixels of auxiliary images in MSIs, after which the remaining subpixels are classified using information derived from the mixed pixels of the base image in MSIs. An artificial image and two remote sensing images were used to evaluate the performance of the proposed HCPMP algorithm. The experimental results demonstrate that HCPMP successfully applied MSIs to produce SRM maps that are visually closer to the reference images and that have greater accuracy than five existing class-allocation algorithms. Especially, it can produce more accurate SRM maps for high-resolution land-cover classes than low-resolution cases. The algorithm takes slightly less runtime than class allocation using linear optimization techniques. Hence, HCPMP provides a valuable new solution for class allocation in SRM using auxiliary data from MSIs.

Variable resolution virtual reality display system

Abstract: A virtual reality display system that renders images at different resolutions in different parts of a display. Reduces rendering latency by rendering at a lower resolution in selected regions, for example on the sides of a display where human vision has lower resolution than in the center. Pixels in low resolution regions are combined into grid elements, and rendering may generate grid element values rather than individual pixel values. Rendering may use ray casting, rasterization, or both. Variable resolution rendering may be combined with variable level of detail geometry models to further reduce rendering time. Selected objects may be designed as high resolution objects that are rendered at a high resolution even in low resolution display regions.

Fusion of Range and Stereo Data for High-Resolution Scene-Modeling

Abstract: This paper addresses the problem of range-stereo fusion, for the construction of high-resolution depth maps. In particular, we combine low-resolution depth data with high-resolution stereo data, in a maximum a posteriori (MAP) formulation. Unlike existing schemes that build on MRF optimizers, we infer the disparity map from a series of local energy minimization problems that are solved hierarchically, by growing sparse initial disparities obtained from the depth data. The accuracy of the method is not compromised, owing to three properties of the data-term in the energy function. First, it incorporates a new correlation function that is capable of providing refined correlations and disparities, via subpixel correction. Second, the correlation scores rely on an adaptive cost aggregation step, based on the depth data. Third, the stereo and depth likelihoods are adaptively fused, based on the scene texture and camera geometry. These properties lead to a more selective growing process which, unlike previous seed-growing methods, avoids the tendency to propagate incorrect disparities. The proposed method gives rise to an intrinsically efficient algorithm, which runs at 3FPS on 2.0 MP images on a standard desktop computer. The strong performance of the new method is established both by quantitative comparisons with state-of-the-art methods, and by qualitative comparisons using real depth-stereo data-sets.

Automatic Orthorectification of High-Resolution Optical Satellite Images Using Vector Roads

Abstract: This paper presents a completely automatic processing chain for orthorectification of optical pushbroom sensors. The procedure is robust and works without manual intervention from raw satellite image to orthoimage. It is modularly divided in four main steps: metadata extraction, automatic ground control point (GCP) extraction, geometric modeling, and orthorectification. The GCP extraction step uses georeferenced vector roads as a reference and produces a file with a list of points and their accuracy estimation. The physical geometric model is based on collinearity equations and works with sensor-corrected (level 1) optical satellite images. It models the sensor position and attitude with second-order piecewise polynomials depending on the acquisition time. The exterior orientation parameters are estimated in a least squares adjustment, employing random sample consensus and robust estimation algorithms for the removal of erroneous points and fine-tuning of the results. The images are finally orthorectified using a digital elevation model and positioned in a national coordinate system. The usability of the method is presented by testing three RapidEye images of regions with different terrain configurations. Several tests were carried out to verify the efficiency of the procedure and to make it more robust. Using the geometric model, subpixel accuracy on independent check points was achieved, and positional accuracy of orthoimages was around one pixel. The proposed procedure is general and can be easily adapted to various sensors.

Fast and Precise 3D Fluorophore Localization based on Gradient Fitting.

Abstract: Astigmatism imaging approach has been widely used to encode the fluorophore's 3D position in single-particle tracking and super-resolution localization microscopy. Here, we present a new high-speed localization algorithm based on gradient fitting to precisely decode the 3D subpixel position of the fluorophore. This algebraic algorithm determines the center of the fluorescent emitter by finding the position with the best-fit gradient direction distribution to the measured point spread function (PSF), and can retrieve the 3D subpixel position of the fluorophore in a single iteration. Through numerical simulation and experiments with mammalian cells, we demonstrate that our algorithm yields comparable localization precision to the traditional iterative Gaussian function fitting (GF) based method, while exhibits over two orders-of-magnitude faster execution speed. Our algorithm is a promising high-speed analyzing method for 3D particle tracking and super-resolution localization microscopy.

A Physics-Based Unmixing Method to Estimate Subpixel Temperatures on Mixed Pixels

Abstract: This paper presents a new algorithm for the analysis of linear spectral mixtures in the thermal infrared domain, with the goal to jointly estimate the abundance and the subpixel temperature in a mixed pixel, i.e., to estimate the relative proportion and the temperature of each material composing the mixed pixel. This novel approach is a two-step procedure. First, it estimates the emissivity and the temperature over pure pixels using the standard temperature and emissivity separation (TES) algorithm. Second, it estimates the abundance and the subpixel temperature using a new unmixing physics-based model, called Thermal Remote sensing Unmixing for Subpixel Temperature (TRUST). This model is based on an estimator of the subpixel temperature obtained by linearizing the black body law around the mean temperature of each material. The abundance is then retrieved by minimizing the reconstruction error with the estimation of the subpixel temperatures. The TRUST method is benchmarked on simulated scenes against the fully constrained least squares unmixing applied on the radiance and on the estimation of surface emissivity using the TES algorithm. The TRUST method shows better results on pure and mixed pixels composed of two materials. TRUST also shows promising results when applied on thermal hyperspectral data acquired with the Thermal Airborne Spectrographic Imager during the Detection in Urban scenario using Combined Airborne imaging Sensors campaign and estimates coherent localization of mixed-pixel areas.

Three-axis OIS for super-resolution imaging

Abstract: Systems and methods are provided for super-resolution imaging using 3-axis OIS. A super-resolution image may be created by enabling optical image stabilization (OIS) in three axes using an OIS system on a camera of an image capturing device; capturing an image of a scene using an image sensor of the camera; shifting the image on the image sensor by a predetermined subpixel amount; capturing the subpixel shifted image; and constructing a super-resolution image of the scene using the image and the subpixel shifted image. In one particular implementation, a position sensor may measure a positional drift of the image sensor after capturing the image. Using this measured positional drift, a time sufficient to shift the image sensor by a predetermined subpixel amount may be determined. The OIS may subsequently be disabled in one or two axes for the determined time.

Frustum-traced raster shadows: revisiting irregular z-buffers

Abstract: We present a real-time system that renders antialiased hard shadows using irregular z-buffers (IZBs). For subpixel accuracy, we use 32 samples per pixel at roughly twice the cost of a single sample. Our system remains interactive on a variety of game assets and CAD models while running at 1080p and 2160p and imposes no constraints on light, camera or geometry, allowing fully dynamic scenes without precomputation. Unlike shadow maps we introduce no spatial or temporal aliasing, smoothly animating even subpixel shadows from grass or wires.Prior irregular z-buffer work relies heavily on GPU compute. Instead we leverage the graphics pipeline, including hardware conservative raster and early-z culling. We observe a duality between irregular z-buffer performance and shadow map quality; this allows common shadow map algorithms to reduce our cost. Compared to state-of-the-art ray tracers, we spawn similar numbers of triangle intersections per pixel yet completely rebuild our data structure in under 2 ms per frame.

Comparison of local descriptors for automatic remote sensing image registration

Abstract: Optical remote sensing (RS) images captured in different conditions might exhibit nonlinear changes. The registration of theses image is an important process. In this paper, we evaluate the performance of the three most successful state-of-the-art descriptors in a feature-based registration process. We have separated the detector from the descriptor as their performance depends on the position of the detected features. The descriptors are compared according to their Recall and runtime efficiency and these deals with several geometric and photometric changes. We also proposed an optimization to the SURF algorithm for color images, called O-SURF, which is a combination of the MSER detector and the SURF descriptor. The results show the effectiveness of proposed improvements compared to base SURF version. Finally, based on the test results, we propose an approach to register automatically optical RS images with subpixel accuracy.

Conductive film, display device provided with same, and method for evaluating conductive film

Abstract: A conductive film is such that, when a display unit is one in which subpixel shapes regarding two colors differing from each other are different, subpixel layout pattern cycles of each color are different, or the center of gravity of one subpixel in one pixel exists at a position differing from a line linking the centers of gravity of remaining other subpixels, a wiring pattern is such that, in the frequency and intensity of moire per color respectively calculated from the peak frequency and peak intensity of the two-dimensional Fourier spectrum of each of the transmittance image data of the wiring pattern and the luminosity image data of the subpixel layout pattern of each color, a moire evaluation index calculated from a moire evaluation value obtained by having the visual response characteristic of a human being act, in accordance with an observation distance, upon moire intensity at a frequency of each moire less than or equal to the maximum frequency of moire defined in accordance with the display resolution of the display unit is less than or equal to a prescribed value.

A new method based on the subpixel Gaussian model for accurate estimation of asteroid coordinates

Abstract: We describe a new iteration method to estimate asteroid coordinates, which is based on the subpixel Gaussian model of a discrete object image. The method operates by continuous parameters (asteroid coordinates) in a discrete observation al space (the set of pixels potential) of the CCD frame. In this model, a kind of the coordinate distribution of the photons hitting a pixel of the CCD frame is known a priori, while the associated parameters are determined from a real digital object image. The developed method, being more flexible in adapting to any form of the object image, has a high measurement accuracy along with a low calculating complexity due to a maximum likelihood procedure, which is implemented to obtain the best fit instead of a least-squares method and Levenberg-Marquar dt algorithm for the minimisation of the quadratic form. Since 2010, the method was tested as the basis of our CoLiTec (Collection Light Technology) software, which has been installed at several observatories of the world with the aim of automatic discoveries of asteroids and comets on a set of CCD frames. As the result, four comets (C/2010 X1 (Elenin), P/2011 NO1(Elenin), C/2012 S1 (ISON), and P/2013 V3 (Nevski)) as well as more than 1500 small Solar System bodies (including five NEOs, 21 Trojan asteroids of Jupiter, and one Centaur object) were discovered. We discuss these results that allowed us to compare the accuracy parameters of a new method and confirm its efficiency. In 2014, the CoLiTec software was recommended to all members of the Gaia-FUN-SSO network for analysing observations as a tool to detect faint moving objects in frames.

Advances in hyperspectral and multispectral image fusion and spectral unmixing

Abstract: . In this work, we jointly process high spectral and high geometric resolution images and exploit their synergies to (a) generate a fused image of high spectral and geometric resolution; and (b) improve (linear) spectral unmixing of hyperspectral endmembers at subpixel level w.r.t. the pixel size of the hyperspectral image. We assume that the two images are radiometrically corrected and geometrically co-registered. The scientific contributions of this work are (a) a simultaneous approach to image fusion and hyperspectral unmixing, (b) enforcing several physically plausible constraints during unmixing that are all well-known, but typically not used in combination, and (c) the use of efficient, state-of-the-art mathematical optimization tools to implement the processing. The results of our joint fusion and unmixing has the potential to enable more accurate and detailed semantic interpretation of objects and their properties in hyperspectral and multispectral images, with applications in environmental mapping, monitoring and change detection. In our experiments, the proposed method always improves the fusion compared to competing methods, reducing RMSE between 4% and 53%.

Vibration extraction based on fast NCC algorithm and high-speed camera.

Abstract: In this study, a high-speed camera system is developed to complete the vibration measurement in real time and to overcome the mass introduced by conventional contact measurements. The proposed system consists of a notebook computer and a high-speed camera which can capture the images as many as 1000 frames per second. In order to process the captured images in the computer, the normalized cross-correlation (NCC) template tracking algorithm with subpixel accuracy is introduced. Additionally, a modified local search algorithm based on the NCC is proposed to reduce the computation time and to increase efficiency significantly. The modified algorithm can rapidly accomplish one displacement extraction 10 times faster than the traditional template matching without installing any target panel onto the structures. Two experiments were carried out under laboratory and outdoor conditions to validate the accuracy and efficiency of the system performance in practice. The results demonstrated the high accuracy and efficiency of the camera system in extracting vibrating signals.

Organic light emitting display and method of driving the same

Abstract: In one aspect, there is an organic light emitting display comprising: a display panel including subpixels; a data driver that supplies a data signal to the display panel; a scan driver that supplies a scan signal to the display panel; and a sensing circuit unit that measures the threshold voltages of driving transistors through sensor transistors of the display panel and prepares compensation data, wherein the scan driver turns on the sensor transistor of a selected subpixel to measure the threshold voltage of the driving transistor of the selected subpixel during a vertical blank interval of the display panel, and turns on the sensor transistors of non-selected subpixels to supply voltages below the threshold voltage of organic light emitting diodes to the non-selected subpixels during an image display interval of the display panel.

Progressive Band Processing of Constrained Energy Minimization for Subpixel Detection

Abstract: Constrained energy minimization (CEM) has been widely used for subpixel detection. It takes advantage of inverting the global sample correlation matrix R to suppress background so as to enhance detection of targets of interest. This paper presents a progressive band processing of CEM (PBP-CEM) which can perform CEM for target detection progressively band by band according to band sequential format. In doing so, a new concept, called causal band correlation matrix (CBCM), is introduced to replace the global sample correlation matrix R. It is a global correlation matrix formed by only those bands that were already visited up to the band currently being processed while excluding bands yet to be visited in the future. The proposed PBP-CEM allows CEM to be processed whenever bands are available, without waiting for completing band collection. With such an advantage, CEM has potential in data transmission and communication, specifically in satellite data processing.

3D Synthesis and Crosstalk Reduction for Lenticular Autostereoscopic Displays

Abstract: Novel image processing methods are presented in this work for 3D synthesis of multiview images and crosstalk reduction to improve the perceived image quality of lenticular autostereoscopic displays. First, for optimizing the intensity of a screen subpixel mapped to a fraction view number, a weighting method is proposed to blend the intensities of the corresponding subpixels from the two neighboring integer view images by minimizing the mean square error. Experimental results show that, comparing with the conventional rounding method, the proposed weighting method can effectively reduce the ghosting artifacts and sharpen the object boundaries when viewing at optimal integer viewpoints. Second, the crosstalk among vertical neighboring subpixels is modeled as a shift-invariant low-pass filter, and a novel computational efficient inverse filtering method is proposed for crosstalk reduction by applying fast Fourier transform (FFT) on each column of subpixels in the multiview interlaced screen image. In addition, a novel filtering method is proposed for determining the maximum input dynamic range of screen subpixel intensities. Experimental results demonstrate that the ghost image from neighboring views can be substantially diminished by the proposed inverse filtering method.

An Improved Phase Correlation Method Based on 2-D Plane Fitting and the Maximum Kernel Density Estimator

Abstract: In this letter, an improved phase correlation (PC) method based on 2-D plane fitting and the maximum kernel density estimator (MKDE) is proposed, which combines the idea of Stone's method and robust estimator MKDE. The proposed PC method first utilizes a vector filter to minimize the noise errors of the phase angle matrix and then unwraps the filtered phase angle matrix by the use of the minimum cost network flow unwrapping algorithm. Afterward, the unwrapped phase angle matrix is robustly fitted via MKDE, and the slope coefficients of the 2-D plane indicate the subpixel shifts between images. The experiments revealed that the improved method can effectively avoid the impact of outliers on the phase angle matrix during the plane fitting and is robust to aliasing and noise. The matching accuracy can reach 1/50th of a pixel using simulated data. The real image sequence tracking experiment was also undertaken to demonstrate the effectiveness of the proposed PC method with a registration accuracy of root-mean-square error better than 0.1 pixels.

Pixel array, electro optical device, electric apparatus and pixel rendering method

Abstract: Provided is a pixel array having a pixel arrangement structure in which a subpixel of the first color having the highest luminosity factor, a subpixel of the second color and a subpixel of the third color having the lowest luminosity factor are arranged in matrix, a row including the subpixel of the first color and the subpixel of the second color that are alternately arranged and a row including the subpixel of the first color and the subpixel of the third color that are alternately arranged are alternately arranged, and a column including the subpixel of the first color and the subpixel of the second color that are alternately arranged and a column including the subpixel of the first color and the subpixel of the third color that are alternately arranged are alternately arranged. The row including the subpixels of the first color and the third color is higher than the row including the subpixels of the first color and the second color, and the subpixel of the first color in the row including the subpixels of the first color and the second color has an area of a light-emitting region substantially equal to that in the subpixel of the first color in the row including the subpixels of the first color and the third color.

Evaluation of Topographic Correction on Subpixel Impervious Cover Mapping With CBERS-2B Data

Abstract: This study compared the effectiveness of six commonly used topographic correction methods for subpixel impervious surface mapping in selected mountainous areas of Southwest Virginia. One 2008 China–Brazil Earth Remote Sensing 2B (CBERS-2B) image was processed using selected topographic algorithms and then used as input for subpixel impervious cover mapping. High-resolution National Agriculture Imagery Program (1-m resolution) images were used to build proportional subpixel impervious cover as training/validation data. We then applied a classification and regression tree algorithm to establish relationships between CBERS signals and impervious surfaces. Accuracy assessment showed that both $R^{2} $ (0.644–0.767) and RMSE (0.118–0.150, reported as proportion of impervious surface) values vary across different topographic correction algorithms. The accuracy differences ( $R^{2} $ : 0.448–0.771; RMSE: 0.118–0.247) were most pronounced for areas facing away from the sun azimuth angle, suggesting aspect–sun azimuth-dependent map accuracy. For terrain shadowing areas, the Minnaert method, the minslope method, and the C-correction substantially outperformed the cosine and improved cosine correction. These findings indicate that users should apply caution in using topographic correction algorithms and that aspect-stratified accuracy assessment needs to be conducted for detailed comparisons. We also repeated the analyses using Landsat TM and obtained better overall results compared to the CBERS-2B data. The differences in $R^{2} $ (or RMSE) for two data sources were not substantial, suggesting the high potential of CBERS data for subpixel impervious surface mapping.

Pixel structure and manufacture method thereof

Abstract: The invention provides a pixel structure and manufacture method thereof The pixel structure comprises a substrate and pixel units on the substrate Each of the pixel unit comprises a pixel circuit structural layer, a pixel definition layer, a pixel electrode, inorganic LED subpixel and a first OLED subpixel The pixle circuit structural layer is formed on the surface of the substrate; the pixel definition layer is formed on the pixel circuit structural layer and has a first opening and a second opening; the pixel electrode is disposed in the first and second openings and electrically connected with the pixle circuit structural layer; the inorganic LED subpixel comprises an ahesion layer, a first semiconductor layer, a second semiconductor layer; the first OLED subpixel comprises a first transmission layer and a second transmission layer; beam emitted by the inorganic LED subpixel has a different color from that of beam emitted by the first OLED subpixel The pixel structure can solve problems that blue light OLED subpixel has a relatively low luminous efficiency, and that red light OLED has a relatively low luminous efficiency after miniaturation

Long range detection of line-array multi-pulsed coding lidar by combining the Accumulation coherence and Subpixel-energy detection method

Abstract: This paper presents a multi-pulsed line-array push broom lidar, the pixel array scale reaches Geiger mode detectors in time-of-flight (TOF) depth imaging: by using time and space correlation between array elements of array avalanche photo detector (APD), light coding technology and a diode pumped solid-state laser with 10kHz repetition rate and 5µJ per pulses. Two signal enhancement methods, accumulation-coherence and high accuracy energy detection were combined improves the decode effect and realizes further long detection range. Experimental results and theory analysis indicating that the retrieval and denoising results of both simulated and real signals demonstrate that our method is practical and effective; what's more, the increasing scale of array sensor and the code bits can further improve system performance.

Superresolution Land-Cover Mapping Based on High-Accuracy Surface Modeling

Abstract: A new superresolution mapping (SRM) method based on high-accuracy surface modeling (HASM) is proposed to generate land-cover maps at the subpixel scale. HASM uses the fundamental theorem of surfaces to uniquely define a land surface, which can produce less errors in interpolation results than classic methods, and thus, the proposed SRM method first uses it to estimate the soft class values of subpixels according to the fraction images of soft classification. Then, it transforms the soft class values into a hard-classified land-cover map using class allocation under the constraints of fraction images. Experiments on a synthetic image and a real remote sensing image show that the proposed method produces more accurate SRM maps than four existing SRM methods. Hence, the proposed method provides a new option for superresolution land-cover mapping.