An Approach towards Measurement of Color Shifting in Misregistration Print Defect using Euclidean and Manhattan Distance Metrics

Misregistration print defect occurs during offset printing affects the visual appearance of printed image. Registration refers to the proper superimposition of colors whereas improper alignment or shifting of colors is resulted as blurred image. Generally registration marks is used in printed sheet to detect this kind of print problem manually. In the presented study a computer vision technique is applied to detect and quantify the problem. Euclidean distance and Manhattan distance measurement method is applied for quantification of color shifting. Therefore this presented study is a novel approach in printing industry which can be a substitute of the usual human perception based method. &nbsp

Impact of intraband misregistration on image classification

Remote sensing data acquired from spaceborne platforms in multispectral channels with moderate to high spatial resolution has been extensively used for numerous applications. Registration between images as well as multispectral bands significantly affects the classification accuracy. Data acquired in multiple channels needs accurate intraband registration to minimise classification errors. Availability of very high spatial resolution data such as from SPOT, IRS-P6, IKONOS, and Quickbird demands very accurate intraband registration. Ability to provide accurate intraband registration requires proper knowledge of satellite attitude, Earth rotation correction, sensor geometry etc. While every effort is made to minimise the intraband misregistration at product generation level, it is difficult to remove it all together. In view of this and its significance on remote sensing image classification, an attempt was made to evaluate the impact of intraband misregistration on classification of remote sensing image with high spatial resolution data. Study carried using a prototype image and IRS-P6 LISS-IV image reveals that image data with intraband misregistration greater than 20% significantly reduce image sharpness and leads to misclassification. Though misregistration of NIR band has major impact on classification it was also seen that misregistration among all bands would lead to even greater error in classification and increased edge blurring

Performance appraisal of VAS radiometry for GOES-4, -5 and -6

The first three VISSR Atmospheric Sounders (VAS) were launched on GOES-4, -5, and -6 in 1980, 1981 and 1983. Postlaunch radiometric performance is assessed for noise, biases, registration and reliability, with special attention to calibration and problems in the data processing chain. The postlaunch performance of the VAS radiometer meets its prelaunch design specifications, particularly those related to image formation and noise reduction. The best instrument is carried on GOES-5, currently operational as GOES-EAST. Single sample noise is lower than expected, especially for the small longwave and large shortwave detectors. Detector to detector offsets are correctable to within the resolution limits of the instrument. Truncation, zero point and droop errors are insignificant. Absolute calibration errors, estimated from HIRS and from radiation transfer calculations, indicate moderate, but stable biases. Relative calibration errors from scanline to scanline are noticeable, but meet sounding requirements for temporarily and spatially averaged sounding fields of view. The VAS instrument is a potentially useful radiometer for mesoscale sounding operations. Image quality is very good. Soundings derived from quality controlled data meet prelaunch requirements when calculated with noise and bias resistant algorithms

Spectral misregistration correction and simulation for hyperspectral imagery

Radiometrically calibrated radiance hyperspectral images can be converted into reflectance images using atmospheric correction in order to extract useful ground information. There are some artifacts in the converted reflectance images due to spectrally misregistered sensor and atmospheric model error. These artifacts give coherent saw-tooth effects in the spectra of the reflectance imagery. These effects degrade the performance of classification and target detection algorithms and make them difficult to compare with ground target spectra. Three spectral misregistration compensation methods were developed in order to compensate for the consistent noise effects. If a ground truth spectrum exists for a test image, the ground truth spectrum can be divided by the spectrum derived from the reflectance image. This will give a coefficient indicating the difference between the ground truth spectrum and the noisy spectrum in the reflectance image. Multiplying this coefficient spectrum and the reflectance image spectrum can correct the saw-tooth effects. The other methods use the Cubic Spline smoothing technique. Cubic Spline smoothing is a fitting algorithm with a non-local smoothing method. Cubic spline smoothing can smooth out the saw-tooth noise in the spectra then the correction coefficient can be calculated as describe above. It is important to find relatively pure and unmixed pixels for the correction coefficient. Two methods for identifying relatively pure pixels were used for this research. The first is the Uniform Region method that is to identify the pixels with small standard deviation values among neighbor pixels. The second method is the Least Ratio method that is used to calculate ratios (standard deviation between smoothed and non-smoothed spectra divided by average reflectance of the spectra) and then calculate the correction coefficient using pixels having small ratios. Spectral misregistration was also simulated using MODTRAN lookup table and DIRSIG (The Digital Imaging and Remote Sensing Image Generation) synthetic image to understand and characterize the effect of spectral misregistration. The spectral misregistration compensation algorithms were tested and verified by the performance measurement of classification and target detection algorithms for test images (real and synthetic images)

An Automated Approach for Sub-Pixel Registration of Landsat-8 Operational Land Imager (OLI) and Sentinel-2 Multi Spectral Instrument (MSI) Imagery

Moderate spatial resolution satellite data from the Landsat-8 OLI and Sentinel-2A MSI sensors together offer 10 m to 30 m multi-spectral reflective wavelength global coverage, providing the opportunity for improved combined sensor mapping and monitoring of the Earth’s surface. However, the standard geolocated Landsat-8 OLI L1T and Sentinel-2A MSI L1C data products are currently found to be misaligned. An approach for automated registration of Landsat-8 OLI L1T and Sentinel-2A MSI L1C data is presented and demonstrated using contemporaneous sensor data. The approach is computationally efficient because it implements feature point detection across four image pyramid levels to identify a sparse set of tie-points. Area-based least squares matching around the feature points with mismatch detection across the image pyramid levels is undertaken to provide reliable tie-points. The approach was assessed by examination of extracted tie-point spatial distributions and tie-point mapping transformations (translation, affine and second order polynomial), dense-matching prediction-error assessment, and by visual registration assessment. Two test sites over Cape Town and Limpopo province in South Africa that contained cloud and shadows were selected. A Landsat-8 L1T image and two Sentinel-2A L1C images sensed 16 and 26 days later were registered (Cape Town) to examine the robustness of the algorithm to surface, atmosphere and cloud changes, in addition to the registration of a Landsat-8 L1T and Sentinel-2A L1C image pair sensed 4 days apart (Limpopo province). The automatically extracted tie-points revealed sensor misregistration greater than one 30 m Landsat-8 pixel dimension for the two Cape Town image pairs, and greater than one 10 m Sentinel-2A pixel dimension for the Limpopo image pair. Transformation fitting assessments showed that the misregistration can be effectively characterized by an affine transformation. Hundreds of automatically located tie-points were extracted and had affine-transformation root-mean-square error fits of approximately 0.3 pixels at 10 m resolution and dense-matching prediction errors of similar magnitude. These results and visual assessment of the affine transformed data indicate that the methodology provides sub-pixel registration performance required for meaningful Landsat-8 OLI and Sentinel-2A MSI data comparison and combined data applications

Experimental study of digital image processing techniques for LANDSAT data

The author has identified the following significant results. Results are reported for: (1) subscene registration, (2) full scene rectification and registration, (3) resampling techniques, (4) and ground control point (GCP) extraction. Subscenes (354 pixels x 234 lines) were registered to approximately 1/4 pixel accuracy and evaluated by change detection imagery for three cases: (1) bulk data registration, (2) precision correction of a reference subscene using GCP data, and (3) independently precision processed subscenes. Full scene rectification and registration results were evaluated by using a correlation technique to measure registration errors of 0.3 pixel rms thoughout the full scene. Resampling evaluations of nearest neighbor and TRW cubic convolution processed data included change detection imagery and feature classification. Resampled data were also evaluated for an MSS scene containing specular solar reflections

White Matter Structural Connectivity is Associated with Sensorimotor Function in Stroke Survivors

Purpose Diffusion tensor imaging (DTI) provides functionally relevant information about white matter structure. Local anatomical connectivity information combined with fractional anisotropy (FA) and mean diffusivity (MD) may predict functional outcomes in stroke survivors. Imaging methods for predicting functional outcomes in stroke survivors are not well established. This work uses DTI to objectively assess the effects of a stroke lesion on white matter structure and sensorimotor function. Methods A voxel-based approach is introduced to assess a stroke lesion\u27s global impact on motor function. Anatomical T1-weighted and diffusion tensor images of the brain were acquired for nineteen subjects (10 post-stroke and 9 age-matched controls). A manually selected volume of interest was used to alleviate the effects of stroke lesions on image registration. Images from all subjects were registered to the images of the control subject that was anatomically closest to Talairach space. Each subject\u27s transformed image was uniformly seeded for DTI tractography. Each seed was inversely transformed into the individual subject space, where DTI tractography was conducted and then the results were transformed back to the reference space. A voxel-wise connectivity matrix was constructed from the fibers, which was then used to calculate the number of directly and indirectly connected neighbors of each voxel. A novel voxel-wise indirect structural connectivity (VISC) index was computed as the average number of direct connections to a voxel\u27s indirect neighbors. Voxel-based analyses (VBA) were performed to compare VISC, FA, and MD for the detection of lesion-induced changes in sensorimotor function. For each voxel, a t-value was computed from the differences between each stroke brain and the 9 controls. A series of linear regressions was performed between Fugl-Meyer (FM) assessment scores of sensorimotor impairment and each DTI metric\u27s log number of voxels that differed from the control group. Results Correlation between the logarithm of the number of significant voxels in the ipsilesional hemisphere and total Fugl-Meyer score was moderate for MD (R2 = 0.512), and greater for VISC (R2 = 0.796) and FA (R2 = 0.674). The slopes of FA (p = 0.0036), VISC (p = 0.0005), and MD (p = 0.0199) versus the total FM score were significant. However, these correlations were driven by the upper extremity motor component of the FM score (VISC: R2 = 0.879) with little influence of the lower extremity motor component (FA: R2 = 0.177). Conclusion The results suggest that a voxel-wise metric based on DTI tractography can predict upper extremity sensorimotor function of stroke survivors, and that supraspinal intraconnectivity may have a less dominant role in lower extremity function

A Comparative Analysis of Hyperspectral Target Detection Algorithms in the Presence of Misregistered Data

Line scanning hyperspectral imaging systems are capable of capturing accurate spatial and spectral information about a scene. These data can be useful for detecting sub-pixel targets. Such systems, however, may be limited by certain key characteristics in their design. Systems employing multiple spectrometers, or that collect data from multiple focal planes may suffer an inherent misregistration between sets of collected spectral bands. In order to utilize the full spectrum for target detection purposes, the sets of bands must be registered to each other as precisely as possible. Perfect registration is not possible, due to both the sensor design, and variation in sensor orientation during data acquisition. The issue can cause degradation in the performance of various target detection algorithms. An analysis of algorithms is necessary to determine which perform well when working with misregistered data. In addition, new algorithms may need to be developed which are more robust in these conditions. The work set forth in this thesis will improve the registration between spectral bands in a line scanning hyperspectral sensor by using a geometric model of the sensor along with aircraft orientation parameters to pair sets of image pixels based on their ground locations. Synthetic scenes were created and band-to-band misregistration was induced between the VIS and NIR spectral channels to test the performance of various hyperspectral target detection algorithms when applied to misregistered hyperspectral data. The results for this case studied show geometric algorithms perform well using only the VIS portion of the EM spectrum, and do not always benefit from the addition of NIR bands, even for small amounts of misregistration. Stochastic algorithms appear to be more robust than geometric algorithms for datasets with band-to-band misregistration. The stochastic algorithms tested often benefit from the addition of NIR bands, even for large amounts of misregistration