scholarly journals Extraction of Sea Ice Cover by Sentinel-1 SAR Based on SVM with Unsupervised Generation of Training Data

2020 ◽  
Author(s):  
Xiaoming Li ◽  
Yan Sun ◽  
Qiang Zhang
Keyword(s):  
Machine Learning ◽  
Sea Ice ◽  
Learning Algorithm ◽  
Texture Features ◽  
Open Water ◽  
Ice Cover ◽  
Training Data ◽  
Support Vector ◽  
Training Samples

In this paper, we focus on developing a novel method to extract sea ice cover (i.e., discrimination/classification of sea ice and open water) using Sentinel-1 (S1) cross-polarization (vertical-horizontal, VH or horizontal-vertical, HV) data in extra wide (EW) swath mode based on the machine learning algorithm support vector machine (SVM). The classification basis includes the S1 radar backscatter coefficients and texture features that are calculated from S1 data using the gray level co-occurrence matrix (GLCM). Different from previous methods where appropriate samples are manually selected to train the SVM to classify sea ice and open water, we proposed a method of unsupervised generation of the training samples based on two GLCM texture features, i.e. entropy and homogeneity, that have contrasting characteristics on sea ice and open water. We eliminate the most uncertainty of selecting training samples in machine learning and achieve automatic classification of sea ice and open water by using S1 EW data. The comparison shows good agreement between the SAR-derived sea ice cover using the proposed method and a visual inspection, of which the accuracy reaches approximately 90% - 95% based on a few cases. Besides this, compared with the analyzed sea ice cover data Ice Mapping System (IMS) based on 728 S1 EW images, the accuracy of extracted sea ice cover by using S1 data is more than 80%.

A Comparison of Machine Learning Algorithms for the Segmentation and Classification of Snow Micro Penetrometer Profiles on Arctic Sea Ice

2021 ◽  
Author(s):  
Julia Kaltenborn ◽  
Viviane Clay ◽  
Amy R. Macfarlane ◽  
Joshua Michael Lloyd King ◽  
Martin Schneebeli
Keyword(s):  
Machine Learning ◽  
Sea Ice ◽  
Arctic Sea Ice ◽  
Machine Learning Algorithms ◽  
Training Data ◽  
Support Vector ◽  
Snow Layer ◽  
Arctic Sea ◽  
Execution Speed

<p>Snow-layer classification is an essential diagnostic task for a wide variety of cryospheric science and climate research applications. Traditionally, these measurements are made in snow pits, requiring trained operators and a substantial time commitment. The SnowMicroPen (SMP), a portable high-resolution snow penetrometer, has been demonstrated as a capable tool for rapid snow grain classification and layer type segmentation through statistical inversion of its mechanical signal. The manual classification of the SMP profiles requires time and training and becomes infeasible for large datasets.</p><p>Here, we introduce a novel set of SMP measurements collected during the MOSAiC expedition and apply Machine Learning (ML) algorithms to automatically classify and segment SMP profiles of snow on Arctic sea ice. To this end, different supervised and unsupervised ML methods, including Random Forests, Support Vector Machines, Artificial Neural Networks, and k-means Clustering, are compared. A subsequent segmentation of the classified data results in distinct layers and snow grain markers for the SMP profiles. The models are trained with the dataset by King et al. (2020) and the MOSAiC SMP dataset. The MOSAiC dataset is a unique and extensive dataset characterizing seasonal and spatial variation of snow on the central Arctic sea-ice.</p><p>We will test and compare the different algorithms and evaluate the algorithms’ effectiveness based on the need for initial dataset labeling, execution speed, and ease of implementation. In particular, we will compare supervised to unsupervised methods, which are distinguished by their need for labeled training data.</p><p>The implementation of different ML algorithms for SMP profile classification could provide a fast and automatic grain type classification and snow layer segmentation. Based on the gained knowledge from the algorithms’ comparison, a tool can be built to provide scientists from different fields with an immediate SMP profile classification and segmentation. </p><p> </p><p>King, J., Howell, S., Brady, M., Toose, P., Derksen, C., Haas, C., & Beckers, J. (2020). Local-scale variability of snow density on Arctic sea ice. <em>The Cryosphere</em>, <em>14</em>(12), 4323-4339, https://doi.org/10.5194/tc-14-4323-2020.</p>

scholarly journals NLOS Multipath Classification of GNSS Signal Correlation Output Using Machine Learning

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2503
Author(s):  
Taro Suzuki ◽  
Yoshiharu Amano
Keyword(s):  
Machine Learning ◽  
Satellite System ◽  
Training Data ◽  
Support Vector ◽  
Positioning Errors ◽  
Automated Method ◽  
Global Navigation Satellite ◽  
Better Than ◽  
Signal Correlation

This paper proposes a method for detecting non-line-of-sight (NLOS) multipath, which causes large positioning errors in a global navigation satellite system (GNSS). We use GNSS signal correlation output, which is the most primitive GNSS signal processing output, to detect NLOS multipath based on machine learning. The shape of the multi-correlator outputs is distorted due to the NLOS multipath. The features of the shape of the multi-correlator are used to discriminate the NLOS multipath. We implement two supervised learning methods, a support vector machine (SVM) and a neural network (NN), and compare their performance. In addition, we also propose an automated method of collecting training data for LOS and NLOS signals of machine learning. The evaluation of the proposed NLOS detection method in an urban environment confirmed that NN was better than SVM, and 97.7% of NLOS signals were correctly discriminated.

scholarly journals Operational algorithm for ice/water classification on dual-polarized RADARSAT-2 images

2016 ◽  
Author(s):  
Natalia Zakhvatkina ◽  
Anton Korosov ◽  
Stefan Muckenhuber ◽  
Stein Sandven ◽  
Mohamed Babiker
Keyword(s):  
Sea Ice ◽  
Texture Features ◽  
Open Water ◽  
Support Vector ◽  
Technical Problems ◽  
Additional Information ◽  
Franz Josef Land ◽  
Water Classification ◽  
Dual Polarized ◽  
Supervised Image Classification

Abstract. Synthetic aperture radar (SAR) data from RADARSAT-2 (RS2) taken in dual-polarization mode provide additional information for discriminating sea ice and open water compared to single-polarization data. We have developed a fully automatic algorithm to distinguish between open water (rough/calm) and sea ice based on dual-polarized RS2 SAR images. Several technical problems inherent in RS2 data were solved on the pre-processing stage including thermal noise reduction in HV-polarization channel and correction of angular backscatter dependency on HH-polarization. Texture features are used as additional information for supervised image classification based on Support Vector Machines (SVM) approach. The main regions of interest are the ice-covered seas between Greenland and Franz Josef Land. The algorithm has been trained using 24 RS2 scenes acquired during winter months in 2011 and 2012, and validated against the manually derived ice chart product from the Norwegian Meteorological Institute. Between 2013 and 2015, 2705 RS2 scenes have been utilised for validation and the average classification accuracy has been found to be 91 ± 4 %.

scholarly journals Support Vector Machines in Big Data Classification: A Systematic Literature Review

2021 ◽  
Author(s):  
Mohammad Hassan Almaspoor ◽  
Ali Safaei ◽  
Afshin Salajegheh ◽  
Behrouz Minaei-Bidgoli
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Large Scale ◽  
Support Vector ◽  
Research Areas ◽  
Large Scale Data ◽  
Training Samples ◽  
Big Data Classification ◽  
Scale Data

Abstract Classification is one of the most important and widely used issues in machine learning, the purpose of which is to create a rule for grouping data to sets of pre-existing categories is based on a set of training sets. Employed successfully in many scientific and engineering areas, the Support Vector Machine (SVM) is among the most promising methods of classification in machine learning. With the advent of big data, many of the machine learning methods have been challenged by big data characteristics. The standard SVM has been proposed for batch learning in which all data are available at the same time. The SVM has a high time complexity, i.e., increasing the number of training samples will intensify the need for computational resources and memory. Hence, many attempts have been made at SVM compatibility with online learning conditions and use of large-scale data. This paper focuses on the analysis, identification, and classification of existing methods for SVM compatibility with online conditions and large-scale data. These methods might be employed to classify big data and propose research areas for future studies. Considering its advantages, the SVM can be among the first options for compatibility with big data and classification of big data. For this purpose, appropriate techniques should be developed for data preprocessing in order to covert data into an appropriate form for learning. The existing frameworks should also be employed for parallel and distributed processes so that SVMs can be made scalable and properly online to be able to handle big data.

Recognition and Classification of Human Activity by Posture Sensing and Machine Learning

2012 ◽  
Vol 468-471 ◽  
pp. 2916-2919
Author(s):  
Fan Yang ◽  
Yu Chuan Wu
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Human Activity ◽  
Learning Algorithm ◽  
Optimal Parameter ◽  
Support Vector ◽  
Rbf Kernel ◽  
Human Activity Detection ◽  
Near Future

This paper describes how to use a posture sensor to validate human daily activity and by machine learning algorithm - Support Vector Machine (SVM) an outstanding model is built. The optimal parameter σ and c of RBF kernel SVM were obtained by searching automatically. Those kinematic data was carried out through three major steps: wavelet transformation, Principle Component Analysis (PCA) -based dimensionality reduction and k-fold cross-validation, followed by implementing a best classifier to distinguish 6 difference actions. As an activity classifier, the SVM (Support Vector Machine) algorithm is used, and we have achieved over 94.5% of mean accuracy in detecting differential actions. It shows that the verification approach based on the recognition of human activity detection is valuable and will be further explored in the near future.

scholarly journals FEASIBILITY OF MACHINE LEARNING METHODS FOR SEPARATING WOOD AND LEAF POINTS FROM TERRESTRIAL LASER SCANNING DATA

2017 ◽  
Vol IV-2/W4 ◽  
pp. 157-164 ◽  
Author(s):  
D. Wang ◽  
M. Hollaus ◽  
N. Pfeifer
Keyword(s):  
Machine Learning ◽  
Laser Scanning ◽  
Learning Algorithms ◽  
Terrestrial Laser Scanning ◽  
Gaussian Mixture ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Area Index ◽  
Training Samples

Classification of wood and leaf components of trees is an essential prerequisite for deriving vital tree attributes, such as wood mass, leaf area index (LAI) and woody-to-total area. Laser scanning emerges to be a promising solution for such a request. Intensity based approaches are widely proposed, as different components of a tree can feature discriminatory optical properties at the operating wavelengths of a sensor system. For geometry based methods, machine learning algorithms are often used to separate wood and leaf points, by providing proper training samples. However, it remains unclear how the chosen machine learning classifier and features used would influence classification results. To this purpose, we compare four popular machine learning classifiers, namely Support Vector Machine (SVM), Na¨ıve Bayes (NB), Random Forest (RF), and Gaussian Mixture Model (GMM), for separating wood and leaf points from terrestrial laser scanning (TLS) data. Two trees, an <i>Erytrophleum fordii</i> and a <i>Betula pendula</i> (silver birch) are used to test the impacts from classifier, feature set, and training samples. Our results showed that RF is the best model in terms of accuracy, and local density related features are important. Experimental results confirmed the feasibility of machine learning algorithms for the reliable classification of wood and leaf points. It is also noted that our studies are based on isolated trees. Further tests should be performed on more tree species and data from more complex environments.

A Machine Learning Algorithm to Predict Hyperglycemic Cases Induced by PD-1/PD-L1 Inhibitors in the Real World (Preprint)

2021 ◽  
Author(s):  
Jincheng Yang
Keyword(s):  
Machine Learning ◽  
Adverse Reaction ◽  
Prediction Model ◽  
Clinical Decision Making ◽  
Learning Algorithm ◽  
Adverse Event Reporting System ◽  
Training Data ◽  
Support Vector ◽  
Machine Learning Algorithm ◽  
Total Drug

BACKGROUND Diabetes mellitus and cancer are amongst the leading causes of deaths worldwide; hyperglycemia plays a major contributory role in neoplastic transformation risk. Support Vector Machine (SVM) is a type of supervised learning method which analyzes data and recognizes patterns, mainly used for statistical classification and regression. OBJECTIVE From reported adverse events of PD-1 or PD-L1 (programmed death 1 or ligand 1) inhibitors in post-marketing monitoring, we aimed to construct an effective machine learning algorithm to predict the probability of hyperglycemic adverse reaction from PD-1/PD-L1 inhibitors treated patients efficiently and rapidly. METHODS Raw data was downloaded from US Food and Drug Administration Adverse Event Reporting System (FDA FAERS). Signal of relationship between drug and adverse reaction based on disproportionality analysis and Bayesian analysis. A multivariate pattern classification of SVM was used to construct classifier to separate adverse hyperglycemic reaction patients. A 10-fold-3-time cross validation for model setup within training data (80% data) output best parameter values in SVM within R software. The model was validated in each testing data (20% data) and two total drug data, with exactly predictor parameter variables: gamma and nu. RESULTS Total 95918 case files were downloaded from 7 relevant drugs (cemiplimab, avelumab, durvalumab, atezolizumab, pembrolizumab, ipilimumab, nivolumab). The number-type/number-optimization method was selected to optimize model. Both gamma and nu values correlated with case number showed high adjusted r2 in curve regressions (both r2 >0.95). Indexes of accuracy, F1 score, kappa and sensitivity were greatly improved from the prediction model in training data and two total drug data. CONCLUSIONS The SVM prediction model established here can non-invasively and precisely predict occurrence of hyperglycemic adverse drug reaction (ADR) in PD-1/PD-L1 inhibitors treated patients. Such information is vital to overcome ADR and to improve outcomes by distinguish high hyperglycemia-risk patients, and this machine learning algorithm can eventually add value onto clinical decision making. CLINICALTRIAL N/A

A Machine Learning Algorithm to Predict Hyperglycemic Cases Induced by PD-1/PD-L1 Inhibitors in the Real World

2020 ◽  
Author(s):  
Jincheng Yang ◽  
Weilong Lin ◽  
Liming Shi ◽  
Ming Deng ◽  
Wenjing Yang
Keyword(s):  
Machine Learning ◽  
Adverse Reaction ◽  
Prediction Model ◽  
Clinical Decision Making ◽  
Learning Algorithm ◽  
Adverse Event Reporting System ◽  
Training Data ◽  
Support Vector ◽  
Machine Learning Algorithm ◽  
Total Drug

Abstract Background: Diabetes mellitus and cancer are amongst the leading causes of deaths worldwide; hyperglycemia plays a major contributory role in neoplastic transformation risk. From reported adverse events of PD-1 or PD-L1 (programmed death 1 or ligand 1) inhibitors in post-marketing monitoring, we aimed to construct an effective machine learning algorithm to predict the probability of hyperglycemic adverse reaction from PD-1/PD-L1 inhibitors treated patients efficiently and rapidly. Methods: Raw data was downloaded from US Food and Drug Administration Adverse Event Reporting System (FDA FAERS). Signal of relationship between drug and adverse reaction based on disproportionality analysis and Bayesian analysis. A multivariate pattern classification of Support Vector Machine (SVM) was used to construct classifier to separate adverse hyperglycemic reaction patients. A 10-fold-3-time cross validation for model setup within training data (80% data) output best parameter values in SVM within R software. The model was validated in each testing data (20% data) and two total drug data, with exactly predictor parameter variables: gamma and nu. Results: Total 95918 case files were downloaded from 7 relevant drugs (cemiplimab, avelumab, durvalumab, atezolizumab, pembrolizumab, ipilimumab, nivolumab). The number-type/number-optimization method was selected to optimize model. Both gamma and nu values correlated with case number showed high adjusted r2 in curve regressions (both r2 >0.95). Indexes of accuracy, F1 score, kappa and sensitivity were greatly improved from the prediction model in training data and two total drug data. Conclusions: The SVM prediction model established here can non-invasively and precisely predict occurrence of hyperglycemic adverse drug reaction (ADR) in PD-1/PD-L1 inhibitors treated patients. Such information is vital to overcome ADR and to improve outcomes by distinguish high hyperglycemia-risk patients, and this machine learning algorithm can eventually add value onto clinical decision making.

scholarly journals Machine Learning for Visual Navigation of Unmanned Ground Vehicles

2012 ◽  
pp. 1090-1107
Author(s):  
Artem A. Lenskiy ◽  
Jong-Soo Lee
Keyword(s):  
Machine Learning ◽  
Visual Information ◽  
Learning Algorithm ◽  
Texture Features ◽  
Visual Navigation ◽  
Machine Learning Algorithms ◽  
Unmanned Ground Vehicles ◽  
Ground Vehicles ◽  
Cross Country

The use of visual information for the navigation of unmanned ground vehicles in a cross-country environment recently received great attention. However, until now, the use of textural information has been somewhat less effective than color or laser range information. This chapter reviews the recent achievements in cross-country scene segmentation and addresses their shortcomings. It then describes a problem related to classification of high dimensional texture features. Finally, it compares three machine learning algorithms aimed at resolving this problem. The experimental results for each machine learning algorithm with the discussion of comparisons are given at the end of the chapter.

scholarly journals CLADES: A Classification-based Machine Learning Method for Species Delimitation from Population Genetic Data

2018 ◽  
Author(s):  
Jingwen Pei ◽  
Chong Chu ◽  
Xin Li ◽  
Bin Lu ◽  
Yufeng Wu
Keyword(s):  
Machine Learning ◽  
Species Delimitation ◽  
Sequence Data ◽  
Genomic Data ◽  
Genetic Data ◽  
Training Data ◽  
Basic Unit ◽  
Support Vector ◽  
Extensive Simulation ◽  
Training Samples

AbstractSpecies are considered to be the basic unit of ecological and evolutionary studies. Since multi-locus genomic data are becoming increasingly available, there has been considerable interests in the use of DNA sequence data to delimit species. In this paper, we show that machine learning can be used for species delimitation. There exists no species delimitation methods that are based on machine learning. Our method treats the species delimitation problem as a classification problem. It is a problem of identifying the category of a new observation on the basis of training data. Extensive simulation is first conducted over a broad range of evolutionary parameters for training purpose. Each pair of known populations are combined to form training samples with a label of “same species” or “different species”. We use Support Vector Machine (SVM) to train a classifier using a set of summary statistics computed from training samples as features. The trained classifier can classify a test sample to two outcomes: “same species” or “different species”. Given multi-locus genomic data of multiple related organisms or populations, our method (called CLADES) performs species delimitation by first classifying pairs of populations. CLADES then delimits species by maximizing the likelihood of species assignment for multiple populations. CLADES is evaluated through extensive simulation and also tested on real genetic data. We show that CLADES is both accurate and efficient for species delimitation when compared with existing methods. CLADES can be useful especially when existing methods have difficulty in delimitation, e.g. with short species divergence time and gene flow.

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