Towards automated lake ice classification using dual polarization RADARSAT SAR imagery

Lake ice, as one of the most important component of the cryosphere, is a valuable indicator of climate change and variability. The Laurentian Great Lakes are the world’s largest supply of freshwater and their ice cover has a major impact on regional weather and climate, ship navigation, and public safety. Monitoring detailed ice conditions on large lakes requires the use of satellite-borne synthetic aperture radar (SAR) data that provide all-weather sensing capabilities, high resolution, and large spatial coverage. Ice experts at the Canadian Ice Service (CIS) have been manually producing operational Great Lakes image analysis charts based on visual interpretation of the SAR images. Ice services such as the CIS would greatly benefit from the availability of an automated or semi-automated SAR ice classification algorithm. We investigated the performance of the unsupervised segmentation algorithm “glocal” iterative region growing with semantics (IRGS) for lake ice classification using dual polarized RADARSAT-2 imagery. Here, the segmented classes with arbitrary labels are manually labelled based on visual interpretation. IRGS was tested on 26 RADARSAT-2 scenes acquired over Lake Erie during winter 2014, and the results were validated against the manually produced CIS image analysis charts. Analysis of various case studies indicated that the “glocal” IRGS algorithm can provide a reliable ice-water classification using dual polarized images with a high overall accuracy of 90.2%. The improvement of using dual-pol as opposed to single-pol images for ice-water discrimination was also demonstrated. For lake ice type classification, most thin ice types were effectively identified but thick and very thick lake ice were often confused due to the ambiguous relation between backscatter and ice types. Texture features could be included for further improvement. Overall, our “glocal” IRGS classification results are close to visual interpretation by ice analysts and would have expected to be closer if they could draw ice charts at a more detailed level

Enhanced CNN for image denoising

Owing to flexible architectures of deep convolutional neural networks (CNNs), CNNs are successfully used for image denoising. However, they suffer from the following drawbacks: (i) deep network architecture is very difficult to train. (ii) Deeper networks face the challenge of performance saturation. In this study, the authors propose a novel method called enhanced convolutional neural denoising network (ECNDNet). Specifically, they use residual learning and batch normalisation techniques to address the problem of training difficulties and accelerate the convergence of the network. In addition, dilated convolutions are used in the proposed network to enlarge the context information and reduce the computational cost. Extensive experiments demonstrate that the ECNDNet outperforms the state-of-the-art methods for image denoising.Comment: CAAI Transactions on Intelligence Technology[J], 201

Scaling Analysis of the Tensile Strength of Bamboo Fibers Using Weibull Statistics

This study demonstrates the effect of weak-link scaling on the tensile strength of bamboo fibers. The proposed model considers the random nature of fiber strength, which is reflected by using a two-parameter Weibull distribution function. Tension tests were performed on samples that could be scaled in length. The size effects in fiber length on the strength were analyzed based on Weibull statistics. The results verify the use of Weibull parameters from specimen testing for predicting the strength distributions of fibers of longer gauge lengths

Different responses of incidence-weighted and abundance-weighted multiple facets of macroinvertebrate beta diversity to urbanization in a subtropical river system

Urbanization is one of the major drivers of biotic homogenization (i.e., decrease in beta diversity) in freshwater systems. However, only a few studies have simultaneously examined how urbanization affects multiple facets (i. e., taxonomic, functional and phylogenetic) of beta diversity and its underlying ecological drivers in urban river macroinvertebrates. Here, we distinguished the patterns and ecological mechanisms of multiple facets of macroinvertebrate beta diversity weighted by incidence and abundance data in a subtropical river system with a distinct urbanization gradient. We also investigated how total beta diversity patterns stem from replacement versus richness difference among sites. Our results showed that taxonomic and phylogenetic beta diversities weighted by incidence data were primarily driven by replacement of taxa, whereas the richness difference contributed more to multiple facets of beta diversity based on abundance data. Furthermore, multiple facets of beta diversity decreased with urbanization for both incidence-weighted and abundance-weighted data, but the former showed more substantial decreases. Both replacement and richness difference components contributed roughly equally to the decline of incidence-weighted beta diversity. In contrast, the losses of abundanceweighted beta diversity were mainly associated with replacement of taxa. Variation partitioning results revealed that all beta diversity measures based on incidence data were governed primarily by local and land-use variables, whereas spatial variables were more relevant in driving beta diversity weighted by abundance data. Overall, by comparing different facets and components of beta diversity weighted by incidence versus abundance data, we suggest that incidence-weighted data may be more sensitive in portraying the impacts of urbanization on macroinvertebrate diversity. This likely resulted from the fact that incidence-weighted data shows the importance of rare taxa in shaping homogenization induced by urbanization.Peer reviewe

Different responses of incidence-weighted and abundance-weighted multiple facets of macroinvertebrate beta diversity to urbanization in a subtropical river system

Urbanization is one of the major drivers of biotic homogenization (i.e., decrease in beta diversity) in freshwater systems. However, only a few studies have simultaneously examined how urbanization affects multiple facets (i. e., taxonomic, functional and phylogenetic) of beta diversity and its underlying ecological drivers in urban river macroinvertebrates. Here, we distinguished the patterns and ecological mechanisms of multiple facets of macroinvertebrate beta diversity weighted by incidence and abundance data in a subtropical river system with a distinct urbanization gradient. We also investigated how total beta diversity patterns stem from replacement versus richness difference among sites. Our results showed that taxonomic and phylogenetic beta diversities weighted by incidence data were primarily driven by replacement of taxa, whereas the richness difference contributed more to multiple facets of beta diversity based on abundance data. Furthermore, multiple facets of beta diversity decreased with urbanization for both incidence-weighted and abundance-weighted data, but the former showed more substantial decreases. Both replacement and richness difference components contributed roughly equally to the decline of incidence-weighted beta diversity. In contrast, the losses of abundanceweighted beta diversity were mainly associated with replacement of taxa. Variation partitioning results revealed that all beta diversity measures based on incidence data were governed primarily by local and land-use variables, whereas spatial variables were more relevant in driving beta diversity weighted by abundance data. Overall, by comparing different facets and components of beta diversity weighted by incidence versus abundance data, we suggest that incidence-weighted data may be more sensitive in portraying the impacts of urbanization on macroinvertebrate diversity. This likely resulted from the fact that incidence-weighted data shows the importance of rare taxa in shaping homogenization induced by urbanization.Peer reviewe

Comprehensive review on gene mutations contributing to dilated cardiomyopathy

Dilated cardiomyopathy (DCM) is one of the most common primary myocardial diseases. However, to this day, it remains an enigmatic cardiovascular disease (CVD) characterized by ventricular dilatation, which leads to myocardial contractile dysfunction. It is the most common cause of chronic congestive heart failure and the most frequent indication for heart transplantation in young individuals. Genetics and various other factors play significant roles in the progression of dilated cardiomyopathy, and variants in more than 50 genes have been associated with the disease. However, the etiology of a large number of cases remains elusive. Numerous studies have been conducted on the genetic causes of dilated cardiomyopathy. These genetic studies suggest that mutations in genes for fibronectin, cytoskeletal proteins, and myosin in cardiomyocytes play a key role in the development of DCM. In this review, we provide a comprehensive description of the genetic basis, mechanisms, and research advances in genes that have been strongly associated with DCM based on evidence-based medicine. We also emphasize the important role of gene sequencing in therapy for potential early diagnosis and improved clinical management of DCM

The Human Connectome Project's neuroimaging approach

Noninvasive human neuroimaging has yielded many discoveries about the brain. Numerous methodological advances have also occurred, though inertia has slowed their adoption. This paper presents an integrated approach to data acquisition, analysis and sharing that builds upon recent advances, particularly from the Human Connectome Project (HCP). The 'HCP-style' paradigm has seven core tenets: (i) collect multimodal imaging data from many subjects; (ii) acquire data at high spatial and temporal resolution; (iii) preprocess data to minimize distortions, blurring and temporal artifacts; (iv) represent data using the natural geometry of cortical and subcortical structures; (v) accurately align corresponding brain areas across subjects and studies; (vi) analyze data using neurobiologically accurate brain parcellations; and (vii) share published data via user-friendly databases. We illustrate the HCP-style paradigm using existing HCP data sets and provide guidance for future research. Widespread adoption of this paradigm should accelerate progress in understanding the brain in health and disease

Frequency drift in MR spectroscopy at 3T

Purpose: Heating of gradient coils and passive shim components is a common cause of instability in the B-0 field, especially when gradient intensive sequences are used. The aim of the study was to set a benchmark for typical drift encountered during MR spectroscopy (MRS) to assess the need for real-time field-frequency locking on MRI scanners by comparing field drift data from a large number of sites.Method: A standardized protocol was developed for 80 participating sites using 99 3T MR scanners from 3 major vendors. Phantom water signals were acquired before and after an EPI sequence. The protocol consisted of: minimal preparatory imaging; a short pre-fMRI PRESS; a ten-minute fMRI acquisition; and a long post-fMRI PRESS acquisition. Both pre- and post-fMRI PRESS were non-water suppressed. Real-time frequency stabilization/adjustment was switched off when appropriate. Sixty scanners repeated the protocol for a second dataset. In addition, a three-hour post-fMRI MRS acquisition was performed at one site to observe change of gradient temperature and drift rate. Spectral analysis was performed using MATLAB. Frequency drift in pre-fMRI PRESS data were compared with the first 5:20 minutes and the full 30:00 minutes of data after fMRI. Median (interquartile range) drifts were measured and showed in violin plot. Paired t-tests were performed to compare frequency drift pre- and post-fMRI. A simulated in vivo spectrum was generated using FID-A to visualize the effect of the observed frequency drifts. The simulated spectrum was convolved with the frequency trace for the most extreme cases. Impacts of frequency drifts on NAA and GABA were also simulated as a function of linear drift. Data from the repeated protocol were compared with the corresponding first dataset using Pearson's and intraclass correlation coefficients (ICC).Results: Of the data collected from 99 scanners, 4 were excluded due to various reasons. Thus, data from 95 scanners were ultimately analyzed. For the first 5:20 min (64 transients), median (interquartile range) drift was 0.44 (1.29) Hz before fMRI and 0.83 (1.29) Hz after. This increased to 3.15 (4.02) Hz for the full 30 min (360 transients) run. Average drift rates were 0.29 Hz/min before fMRI and 0.43 Hz/min after. Paired t-tests indicated that drift increased after fMRI, as expected (p &lt; 0.05). Simulated spectra convolved with the frequency drift showed that the intensity of the NAA singlet was reduced by up to 26%, 44 % and 18% for GE, Philips and Siemens scanners after fMRI, respectively. ICCs indicated good agreement between datasets acquired on separate days. The single site long acquisition showed drift rate was reduced to 0.03 Hz/min approximately three hours after fMRI.Discussion: This study analyzed frequency drift data from 95 3T MRI scanners. Median levels of drift were relatively low (5-min average under 1 Hz), but the most extreme cases suffered from higher levels of drift. The extent of drift varied across scanners which both linear and nonlinear drifts were observed.</p

Semi-Automated Classification of Lake Ice Cover Using Dual Polarization RADARSAT-2 Imagery

Lake ice is a significant component of the cryosphere due to its large spatial coverage in high-latitude regions during the winter months. The Laurentian Great Lakes are the world&rsquo;s largest supply of freshwater and their ice cover has a major impact on regional weather and climate, ship navigation, and public safety. Ice experts at the Canadian Ice Service (CIS) have been manually producing operational Great Lakes image analysis charts based on visual interpretation of the synthetic aperture radar (SAR) images. In that regard, we have investigated the performance of the semi-automated segmentation algorithm &ldquo;glocal&rdquo; Iterative Region Growing with Semantics (IRGS) for lake ice classification using dual polarized RADARSAT-2 imagery acquired over Lake Erie. Analysis of various case studies indicated that the &ldquo;glocal&rdquo; IRGS algorithm could provide a reliable ice-water classification using dual polarized images with a high overall accuracy of 90.4%. However, lake ice types that are based on stage of development were not effectively identified due to the ambiguous relation between backscatter and ice types. The slight improvement of using dual-pol as opposed to single-pol images for ice-water discrimination was also demonstrated

Lake Ice Classification from Sentinel-1A 2015 - 2018

In the ESA DUE GlobPermafrost project lake ice classifications were produced to distinguish grounded ice and floating ice in lakes in Barrow (Arctic Alaska, US), Teshekpuk (Arctic Alaska, US), Mackenzie Delta (Beaufort Sea Region, CA), Kytalyk (Central Yakutia, RU), Lena Delta (Laptev and East Siberian Sea Region, RU) and Yamal (Western Siberia, RU). Classifications are based on Sentinel-1A synthetic aperture radar time-series with a resolution between 18 m and 26 m. Imagery provides a weekly to monthly resolution throughout April and May for the years 2015 to 2017. Three versions of the product are provided using three different algorithms: 1) image thresholding (THRESH), 2) unsupervised image segmentation using K-means classification (KMEANS), and 3) unsupervised image segmentation using the Iterative Region Growing with Semantics (IRGSEM) algorithm. All versions contain two classes: grounded ice (CLASS 1) and floating ice (CLASS 2). All data are provided in geotiff format. More information about image processing and classification can be found in the product guide