A Comparative Evaluation of Snowflake Particle Size and Shape Estimation Techniques used by the Precipitation Imaging Package (PIP), Multi-Angle Snowflake Camera (MASC), and Two-Dimensional Video Disdrometer (2DVD)

Measurements of snowflake particle size and shape are important for studying the snow microphysics. While a number of instruments exist that are designed to measure these important parameters, this study focuses on the measurement techniques of three digital video disdrometers: the Precipitation Imaging Package (PIP), the Multi-Angle Snowflake Camera (MASC) and the Two-Dimensional Video Disdrometer (2DVD). To gain a better understanding of the relative strengths and weaknesses of these instruments and to provide a foundation upon which comparisons can be made between studies using data from different instruments, we perform a comparative analysis of the measurement algorithms employed by each of the three instruments by applying the algorithms to snowflake images captured by PIP during the ICEP-POP 2018 field campaign. Our analysis primarily focuses on the measurements of area, equivalent diameter, and aspect ratio. Our findings indicate that area and equi-area diameter measurements using the 2DVD camera setup should be the most accurate, followed by MASC, which is slightly more accurate than PIP. In terms of the precision of the area and equi-area diameter measurements, however, MASC is considerably more precise than PIP or 2DVD, which provide similar precision once the effects of the PIP image compression algorithm are taken into account. Both PIP and MASC use shape-fitting algorithms to measure aspect ratio. While our analysis of the MASC aspect ratio suggests the measurements are reliable, our findings indicate that both the ellipse and rectangle aspect ratios produced by PIP under-performed considerably due to the shortcomings of the PIP shape-fitting techniques. That said, we also demonstrate that reliable measurements of aspect ratio can be retrieved from PIP by reprocessing the PIP images using either the MASC shape-fitting technique or a tensor-based ellipse-fitting technique. Because of differences in instrument design, 2DVD produces measurements of particle horizontal and vertical extent rather than length and width. Furthermore, the 2DVD measurements of particle horizontal extent can be contaminated by horizontal particle motion. Our findings indicate that, although the correction technique used to remove the horizontal motion contamination performs remarkably well with snowflakes despite being designed for use with rain drops, the 2DVD measurements of particle horizontal extent are potentially unreliable.

Decadal Measurements of the First Geostationary Ocean Color Satellite (GOCI) Compared with MODIS and VIIRS Data

The first geostationary ocean color data from the Geostationary Ocean Color Imager (GOCI) onboard the Communication, Ocean, and Meteorological Satellite (COMS) have been accumulating for more than ten years from 2010. This study performs a multi-year quality assessment of GOCI chlorophyll-a (Chl-a) and radiometric data for 2012–2021 with an advanced atmospheric correction technique and a regionally specialized Chl-a algorithm. We examine the consistency and stability of GOCI, Moderate Resolution Imaging Spectroradiometer (MODIS), and Visible Infrared Imaging Radiometer Suite (VIIRS) level 2 products in terms of annual and seasonal climatology, two-dimensional frequency distribution, and multi-year time series. Overall, the GOCI agrees well with MODIS and VIIRS on annual and seasonal variability in Chl-a, as the central biological pattern of the most transparent waters over the western North Pacific, productive waters over the East Sea, and turbid waters over the Yellow Sea are reasonably represented. Overall, an excellent agreement is remarkable for western North Pacific oligotrophic waters (with a correlation higher than 0.91 for Chl-a and 0.96 for band-ratio). However, the sporadic springtime overestimation of MODIS Chl-a values compared with others is notable over the Yellow Sea and East Sea due to the underestimation of MODIS blue-green band ratios for moderate-high aerosol optical depth. The persistent underestimation of VIIRS Chl-a values compared with GOCI and MODIS occurs due to inherent sensor calibration differences. In addition, the artificially increasing trends in GOCI Chl-a (+0.48 mg m−3 per 9 years) arise by the decreasing trends in the band ratios. However, decreasing Chl-a trends in MODIS and VIIRS (−0.09 and −0.08 mg m−3, respectively) are reasonable in response to increasing sea surface temperature. The results indicate GOCI sensor degradation in the late mission period. The long-term application of the GOCI data should be done with a caveat, however; planned adjustments to GOCI calibration (2022) in the following GOCI-II satellite will essentially eliminate the bias in Chl-a trends.

A Pipelined Tracer-Aware Approach for Lesion Segmentation in Breast DCE-MRI

The recent spread of Deep Learning (DL) in medical imaging is pushing researchers to explore its suitability for lesion segmentation in Dynamic Contrast-Enhanced Magnetic-Resonance Imaging (DCE-MRI), a complementary imaging procedure increasingly used in breast-cancer analysis. Despite some promising proposed solutions, we argue that a “naive” use of DL may have limited effectiveness as the presence of a contrast agent results in the acquisition of multimodal 4D images requiring thorough processing before training a DL model. We thus propose a pipelined approach where each stage is intended to deal with or to leverage a peculiar characteristic of breast DCE-MRI data: the use of a breast-masking pre-processing to remove non-breast tissues; the use of Three-Time-Points (3TP) slices to effectively highlight contrast agent time course; the application of a motion-correction technique to deal with patient involuntary movements; the leverage of a modified U-Net architecture tailored on the problem; and the introduction of a new “Eras/Epochs” training strategy to handle the unbalanced dataset while performing a strong data augmentation. We compared our pipelined solution against some literature works. The results show that our approach outperforms the competitors by a large margin (+9.13% over our previous solution) while also showing a higher generalization ability.

Atmospheric Correction of Airborne Hyperspectral CASI Data Using Polymer, 6S and FLAASH

Airborne hyperspectral data play an important role in remote sensing of coastal waters. However, before their application, atmospheric correction is required to remove or reduce the atmospheric effects caused by molecular and aerosol scattering and absorption. In this study, we first processed airborne hyperspectral CASI-1500 data acquired on 4 May 2019 over the Uljin coast of Korea with Polymer and then compared the performance with the other two widely used atmospheric correction approaches, i.e., 6S and FLAASH, to determine the most appropriate correction technique for CASI-1500 data in coastal waters. Our results show the superiority of Polymer over 6S and FLAASH in deriving the Rrs spectral shape and magnitude. The performance of Polymer was further evaluated by comparing CASI-1500 Rrs data with those obtained from the MODIS-Aqua sensor on 3 May 2019 and processed using Polymer. The spectral shapes of the derived Rrs from CASI-1500 and MODIS-Aqua matched well, but the magnitude of CASI-1500 Rrs was approximately 0.8 times lower than MODIS Rrs. The possible reasons for this difference were time difference (1 day) between CASI-1500 and MODIS data, higher land adjacency effect for MODIS-Aqua than for CASI-1500, and possible errors in MODIS Rrs from Polymer.

Correcting Imprecise Object Locations for Training Object Detectors in Remote Sensing Applications

Object detection on aerial and satellite imagery is an important tool for image analysis in remote sensing and has many areas of application. As modern object detectors require accurate annotations for training, manual and labor-intensive labeling is necessary. In situations where GPS coordinates for the objects of interest are already available, there is potential to avoid the cumbersome annotation process. Unfortunately, GPS coordinates are often not well-aligned with georectified imagery. These spatial errors can be seen as noise regarding the object locations, which may critically harm the training of object detectors and, ultimately, limit their practical applicability. To overcome this issue, we propose a co-correction technique that allows us to robustly train a neural network with noisy object locations and to transform them toward the true locations. When applied as a preprocessing step on noisy annotations, our method greatly improves the performance of existing object detectors. Our method is applicable in scenarios where the images are only annotated with points roughly indicating object locations, instead of entire bounding boxes providing precise information on the object locations and extents. We test our method on three datasets and achieve a substantial improvement (e.g., 29.6% mAP on the COWC dataset) over existing methods for noise-robust object detection.

Sequential correction technique in degenerative scoliosis with type C coronal imbalance: a comparison with traditional 2-rod technique

OBJECTIVE The aim of this study was to compare the radiographic and clinical outcomes in patients with degenerative scoliosis (DS) with type C coronal imbalance who underwent either a sequential correction technique or a traditional 2-rod technique with a minimum of 2 years of follow-up. METHODS DS patients with type C coronal imbalance undergoing posterior correction surgery from February 2014 to January 2018 were divided into groups by technique: the sequential correction technique (SC group) and the traditional 2-rod technique (TT group). Radiographic parameters, including Cobb angle, coronal balance distance (CBD), global kyphosis (GK), thoracic kyphosis (TK), lumbar lordosis (LL), sagittal vertical axis (SVA), pelvic incidence (PI), pelvic tilt (PT), and sacral slope, were assessed pre- and postoperatively. The SF-36 questionnaire was used to assess quality of life. RESULTS A total of 34 patients were included. Significant postoperative improvement in the Cobb angle of the main curve, CBD, GK, TK, LL, SVA, and PT was found in both groups (p < 0.05). Postoperatively, the coronal balance was type A in 13 patients (92.9%) in the SC group and in 16 patients (80.0%) in the TT group (p = 0.298). In the TT group, 1 patient had deteriorative coronal imbalance immediately postoperatively, and coronal imbalance deteriorated from type A to type C in 2 patients during follow-up. The scores of Physical Functioning, Role-Physical, Bodily Pain, Vitality, Social Functioning, Role-Emotional, and Mental Health were statistically improved postoperatively (p < 0.05) in both groups. Type C coronal imbalance at the last follow-up was associated with a relatively worse quality of life. There were no implant failures during follow-up in the SC group, whereas rod fracture was observed in 3 patients in the TT group. CONCLUSIONS Compared with the traditional 2-rod technique, the sequential correction technique can simplify rod installation procedure, enhance internal instrumentation, and reduce risk of implant failures. The sequential correction technique could be routinely recommended for DS patients with type C coronal imbalance.

A statistical analysis of the differences between rainfall estimated by Chennai DWR and conventional rainfall data on monthly and seasonal scales during the Indian northeast monsoon season

The first Doppler Weather Radar (DWR) of India Meteorological Department has been functional at Chennai since the year 2002 providing various meteorological and hydrological products. Validation and statistical analysis of the DWR estimated rainfall (RERF, x) data with rain gauge measured rainfall (RGRF, y) of 34 land based stations located in the semi-circular land area within 100 km radius of Chennai DWR (CDLR100) has been performed for the northeast monsoon (NEM) season of October-November-December (OND) for the 12 year period 2002-13. The monthly and seasonal data have been derived using more than 1.42 lakh discrete daily RERF values available at a high resolution of 333 m × 333 m. The major objective of the study is to compute the various statistical parameters of x and y including the bias between them on monthly and seasonal scales and to draw certain inferences. The analysis was done using three different types of averaging. The yearly means of x and y for OND over CDLR100 manifested both positive and negative epochs with the mean absolute deviation (MAD) computed as 11 cm (17% of mean). The short term normals over CDLR100 are derived as 274.9, 262.6, 96.5 and 629.8 mm for x and 243.8, 254.6, 128.0 and 627.4 mm for y for October, November, December and OND yielding bias values of -31.2, -8.0, 31.5 and -2.4 mm respectively. The MAD for OND rainfall computed by pooling in all the 12 ´ 34 values is quite substantial at around 19 cm (30% of mean). The RF bias for each month / NEM season is shown to be independent of the geographical locations of the stations using correlation analysis. Based on the raw values of x and a proportional correction technique, estimated values of at the 1.42 lakh grid points of CDLR100 were derived yielding spatial means of 273.3, 262.2, 92.5 and 628.4 mm for x and 243.4, 254.3, 122.9 and 622.1 mm for for the three months and OND respectively. The importance of size of the bias in the correct interpretation of x has been discussed. A few suggestions based on certain statistical considerations have been putforth for decreasing the bias.

Improving Regularization Techniques for Incompressible Fluid Flows via Defect Correction

We propose and investigate two regularization models for fluid flows at higher Reynolds numbers. Both models are based on the reduced ADM regularization (RADM). One model, which we call DC-RADM (deferred correction for reduced approximate deconvolution model), aims to improve the temporal accuracy of the RADM. The second model, denoted by RADC (reduced approximate deconvolution with correction), is created with a more systemic approach. We treat the RADM regularization as a defect in approximating the true solution of the Navier–Stokes equations (NSE) and then correct for this defect, using the defect correction algorithm. Thus, the resulting RADC model can be viewed as a first member of the class that we call “LESC-reduced”, where one starts with a regularization that resembles a Large Eddy Simulation turbulence model and then improves it with a defect correction technique. Both models are investigated theoretically and numerically, and the RADC is shown to outperform the DC-RADM model both in terms of convergence rates and in terms of the quality of the produced solution.