Pixels In Length Research Articles

Multifractal analysis on CT soil images: Fluctuation analysis versus mass distribution

The combined action of physical, chemical and biological processes at different scales creates a highly complex soil architecture. At the same time, this soil pores' complexity is crucial in maintaining soil biogeochemical and biophysical processes. The development of digital image processing and a multiscaling analysis allow a better study to quantify pore complexity and evaluate pore spatial variability. This study took saline soils from coastal reclamation areas as an example and aimed to compare the multifractality of porosity series using different methods on sample cubes of 512 pixels in length extracted at different soil depths. Two measures were selected to represent the porosity series: the average grey values (AVG) of each slice and the CT-porosity (CTP) of binarized slices. The mass distribution (MD), detrended fluctuation analysis (DFA), and the moments function (K) were the methods applied in the multiscaling study. Shuffled and surrogated series were used to analyze the two possible multifractality sources, the probability distribution density function (PDF) and long-range correlations, respectively.The results showed that MD gave the minimum multifractal spectrum, which appeared as a point in AGV and a narrow concave parabola in CTP. On the other hand, both AGV and CTP showed a higher complexity, a clear and significant multifractal behaviour when DFA and K were used. The shuffled and surrogated series pointed out that the long-range correlations were the main factor of the multifractal sources of the porosity series. Both long-range correlations and PDF influenced more heavily on the multifractality of porosity in topsoil (0– 20 cm) than in the deeper soil (20– 60 cm), implying that the porosity series in topsoil had stronger multifractality. In addition, the DFA and K, combined with shuffled and surrogated series analysis, can distinguish between different factors in the multiscaling behaviour of different soil textures and time reclamation.

Selecting the Best Image Pairs to Measure Slope Deformation

Optical remote sensing images can be used to monitor slope deformation in mountain regions. Abundant optical sensors onboard various platforms were designed to provide increasingly high spatial–temporal resolution images at low cost; however, finding the best image pairs to derive slope deformation remains difficult. By selecting a location in the east Tibetan Plateau, this work used the co-registration of optically sensed images and correlation (COSI-Corr) method to analyze 402 Sentinel-2 images from August 2015 to February 2020, to quantify temporal patterns of uncertainty in deriving slope deformation. By excluding 66% of the Sentinel-2 images that were contaminated by unfavorable weather, uncertainties were found to fluctuate annually, with the least uncertainty achieved in image pairs of similar dates in different years. Six image pairs with the least uncertainties were selected to derive ground displacement for a moving slope in the study area. Cross-checks among these image pairs showed consistent results, with uncertainties less than 1/10 pixels in length. The findings from this work could help in the selection of the best image pairs to derive reliable slope displacement from large numbers of optical images.

Vehicle and Vessel Detection on Satellite Imagery: A Comparative Study on Single-Shot Detectors

In this paper, we investigate the feasibility of automatic small object detection, such as vehicles and vessels, in satellite imagery with a spatial resolution between 0.3 and 0.5 m. The main challenges of this task are the small objects, as well as the spread in object sizes, with objects ranging from 5 to a few hundred pixels in length. We first annotated 1500 km2, making sure to have equal amounts of land and water data. On top of this dataset we trained and evaluated four different single-shot object detection networks: YOLOV2, YOLOV3, D-YOLO and YOLT, adjusting the many hyperparameters to achieve maximal accuracy. We performed various experiments to better understand the performance and differences between the models. The best performing model, D-YOLO, reached an average precision of 60% for vehicles and 66% for vessels and can process an image of around 1 Gpx in 14 s. We conclude that these models, if properly tuned, can thus indeed be used to help speed up the workflows of satellite data analysts and to create even bigger datasets, making it possible to train even better models in the future.

Simulation of Ship-Track versus Satellite-Sensor Differences in Oceanic Precipitation Using an Island-Based Radar

The point-to-area problem strongly complicates the validation of satellite-based precipitation estimates, using surface-based point measurements. We simulate the limited spatial representation of light-to-moderate oceanic precipitation rates along ship tracks with respect to areal passive microwave satellite estimates using data from a subtropical island-based radar. The radar data serves to estimate the discrepancy between point-like and areal precipitation measurements. From the spatial discrepancy, two statistical adjustments are derived so that along-track precipitation ship data better represent areal precipitation estimates from satellite sensors. The first statistical adjustment uses the average duration of a precipitation event as seen along a ship track, and the second adjustment uses the median-normalized along-track precipitation rate. Both statistical adjustments combined reduce the root mean squared error by 0.24 mm h − 1 (55%) compared to the unadjusted average track of 60 radar pixels in length corresponding to a typical ship speed of 24–34 km h − 1 depending on track orientation. Beyond along-track averaging, the statistical adjustments represent an important step towards a more accurate validation of precipitation derived from passive microwave satellite sensors using point-like along-track surface precipitation reference data.

Type 1 Inositol (1,4,5)-Trisphosphate Receptor Activates Ryanodine Receptor 1 to Mediate Calcium Spark Signaling in Adult Mammalian Skeletal Muscle

Functional coupling between inositol (1,4,5)-trisphosphate receptor (IP(3)R) and ryanodine receptor (RyR) represents a critical component of intracellular Ca(2+) signaling in many excitable cells; however, the role of this mechanism in skeletal muscle remains elusive. In skeletal muscle, RyR-mediated Ca(2+) sparks are suppressed in resting conditions, whereas application of transient osmotic stress can trigger activation of Ca(2+) sparks that are restricted to the periphery of the fiber. Here we show that onset of these spatially confined Ca(2+) sparks involves interaction between activation of IP(3)R and RyR near the sarcolemmal membrane. Pharmacological prevention of IP(3) production or inhibition of IP(3)R channel activity abolishes stress-induced Ca(2+) sparks in skeletal muscle. Although genetic ablation of the type 2 IP(3)R does not appear to affect Ca(2+) sparks in skeletal muscle, specific silencing of the type 1 IP(3)R leads to ablation of stress-induced Ca(2+) sparks. Our data indicate that membrane-delimited signaling involving cross-talk between IP(3)R1 and RyR1 contributes to Ca(2+) spark activation in skeletal muscle.

Estimating the Microtubule GTP Cap Size In Vivo

Microtubules (MTs) polymerize via net addition of GTP-tubulin subunits to the MT plus end, which subsequently hydrolyze to GDP-tubulin in the MT lattice. Relatively stable GTP-tubulin subunits create a "GTP cap" at the growing MT plus end that suppresses catastrophe. To understand MT assembly regulation, we need to understand GTP hydrolysis reaction kinetics and the GTP cap size. In vitro, the GTP cap has been estimated to be as small as one layer (13 subunits) or as large as 100-200 subunits. GTP cap size estimates in vivo have not yet been reported. Using EB1-EGFP as a marker for GTP-tubulin in epithelial cells, we find on average (1) 270 EB1 dimers bound to growing MT plus ends, and (2) a GTP cap size of ∼750 tubulin subunits. Thus, in vivo, the GTP cap is far larger than previous estimates in vitro, and ∼60-fold larger than a single layer cap. We also find that the tail of a large GTP cap promotes MT rescue and suppresses shortening. We speculate that a large GTP cap provides a locally concentrated scaffold for tip-tracking proteins and confers persistence to assembly in the face of physical barriers such as the cell cortex.

Wigner Distribution for 2D Motion Estimation from Noisy Images

The Wigner distribution has been extensively used for speech processing. However, its use for image processing is new. Past attempts at using the Wigner distribution for motion detection have led to complex and often impractical algorithms. This paper takes a new look at the use of the Wigner distribution, develops necessary theory for its use, presents a new technique for reducing the undesirable cross-term effects, and shows practical applications. Our results are based on 125,000 simulations in a 64 × 64 size frame. The results show that even with a signal-to-noise ratio (SNR) of 5 dB, one can detect rectangular objects of at least 6 pixels in length and width, to within 0.5 pixel location accuracy. The misdetection rate is near zero and the average false detection rate is one false object per frame in a 5-dB SNR environment. This false detection rate is reduced to zero by using the pseudopeak elimination technique developed here. The method is then applied to an image sequence obtained from a real dynamic scene of a 747 airplane taking off. Using the new method, the 747 take-off speed was was predicted to be 142 knots, which is well within the typical range of 140 to 150 knots. Another sequence consisting of cars in motion also gives good results. The results presented here clearly show the successful application of the method to real life situations.

Evaluation of multichannel Wiener Filters applied to fine resolution passive microwave images of first-year sea ice

Over the past two decades passive microwave imaging systems have proven to be effective reconnaissance tools in polar environments. However, the mechanical scan mechanism and high gain electronics characteristic of this class of sensors commonly impart noise and unwanted artifacts to image data they produce, complicating visual analysis and automated classification procedures. The fact that data in individual scan lines are characterized by statistical stationarity and that information in adjacent pixels is highly correlated due to oversampling of these image data suggests that Wiener multichannel filtering techniques may prove effective in this application. Wiener filters applied to passive microwave images of first-year sea ice were constructed. Four major parameters that define the filter (lag or pixel offset between the original and desired scenes, filter length, number of lines in the filter, and weight applied to the empirical correlation functions) were varied. Results were compared visually to assess the effect of each variable on image quality. Effective filters that limit high frequency noise and enhance ice characteristics use a lag of one pixel, consist of two or three channels, are five pixels in length, and weigh the auto- and cross-correlation functions equally.

Treatment and splitting of samples for bacteria and meiofauna biomass determinations by means of a semi-automatic image analysis system

Treatment and splitting of samples for bacteria and meiofauna biomass determinations by means of a semi-automatic image analysis system