AN APPLICATION OF ROLL-INVARIANT POLARIMETRIC FEATURES FOR CROP CLASSIFICATION FROM MULTI-TEMPORAL RADARSAT-2 SAR DATA

Abstract. Crops are dynamically changing and time-critical in the growing season and therefore multitemporal earth observation data are needed for spatio-temporal monitoring of the crops. This study evaluates the impacts of classical roll-invariant polarimetric features such as entropy (H), anisotropy (A), mean alpha angle (&alpha;) and total scattering power (SPAN) for the crop classification from multitemporal polarimetric SAR data. For this purpose, five different data set were generated as following: (1) H&alpha;, (2) H&alpha;Span, (3) H&alpha;A, (4) H&alpha;ASpan and (5) coherency [T] matrix. A time-series of four PolSAR data (Radarsat-2) were acquired as 13 June, 01 July, 31 July and 24 August in 2016 for the test site located in Konya, Turkey. The test site is covered with crops (maize, potato, summer wheat, sunflower, and alfalfa). For the classification of the data set, three different models were used as following: Support Vector Machines (SVMs), Random Forests (RFs) and Naive Bayes (NB). The experimental results highlight that H&alpha;ASpan (91.43% for SVM, 92.25% for RF and 90.55% for NB) outperformed all other data sets in terms of classification performance, which explicitly proves the significant contribution of SPAN for the discrimination of crops. Highest classification accuracy was obtained as 92.25% by RF and H&alpha;ASpan while lowest classification accuracy was obtained as 66.99% by NB and H&alpha;. This experimental study suggests that roll-invariant polarimetric features can be considered as the powerful polarimetric components for the crop classification. In addition, the findings prove the added benefits of PolSAR data investigation by means of crop classification.

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Classification of standing and sitting phases based on in-socket piezoelectric sensors in a transfemoral amputee

Biomedical Engineering / Biomedizinische Technik ◽

10.1515/bmt-2018-0249 ◽

2020 ◽

Vol 0 (0) ◽

Author(s):

Tawfik Yahya ◽

Nur Azah Hamzaid ◽

Sadeeq Ali ◽

Farahiyah Jasni ◽

Hanie Nadia Shasmin

Keyword(s):

Time Domain ◽

Classification Accuracy ◽

Sensory System ◽

Metabolic Energy ◽

Support Vector ◽

Data Set ◽

Transfemoral Amputee ◽

Sit To Stand ◽

Optimal Classifier ◽

The Impact

AbstractA transfemoral prosthesis is required to assist amputees to perform the activity of daily living (ADL). The passive prosthesis has some drawbacks such as utilization of high metabolic energy. In contrast, the active prosthesis consumes less metabolic energy and offers better performance. However, the recent active prosthesis uses surface electromyography as its sensory system which has weak signals with microvolt-level intensity and requires a lot of computation to extract features. This paper focuses on recognizing different phases of sitting and standing of a transfemoral amputee using in-socket piezoelectric-based sensors. 15 piezoelectric film sensors were embedded in the inner socket wall adjacent to the most active regions of the agonist and antagonist knee extensor and flexor muscles, i. e. region with the highest level of muscle contractions of the quadriceps and hamstring. A male transfemoral amputee wore the instrumented socket and was instructed to perform several sitting and standing phases using an armless chair. Data was collected from the 15 embedded sensors and went through signal conditioning circuits. The overlapping analysis window technique was used to segment the data using different window lengths. Fifteen time-domain and frequency-domain features were extracted and new feature sets were obtained based on the feature performance. Eight of the common pattern recognition multiclass classifiers were evaluated and compared. Regression analysis was used to investigate the impact of the number of features and the window lengths on the classifiers’ accuracies, and Analysis of Variance (ANOVA) was used to test significant differences in the classifiers’ performances. The classification accuracy was calculated using k-fold cross-validation method, and 20% of the data set was held out for testing the optimal classifier. The results showed that the feature set (FS-5) consisting of the root mean square (RMS) and the number of peaks (NP) achieved the highest classification accuracy in five classifiers. Support vector machine (SVM) with cubic kernel proved to be the optimal classifier, and it achieved a classification accuracy of 98.33 % using the test data set. Obtaining high classification accuracy using only two time-domain features would significantly reduce the processing time of controlling a prosthesis and eliminate substantial delay. The proposed in-socket sensors used to detect sit-to-stand and stand-to-sit movements could be further integrated with an active knee joint actuation system to produce powered assistance during energy-demanding activities such as sit-to-stand and stair climbing. In future, the system could also be used to accurately predict the intended movement based on their residual limb’s muscle and mechanical behaviour as detected by the in-socket sensory system.

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Multi-Classification of Fetal Health Status Using Extreme Learning Machine

ICONTECH INTERNATIONAL JOURNAL ◽

10.46291/icontechvol5iss2pp62-70 ◽

2021 ◽

Vol 5 (2) ◽

pp. 62-70

Author(s):

Ömer KASIM

Keyword(s):

Extreme Learning Machine ◽

Classification Accuracy ◽

Binary Classification ◽

Clinical Decision ◽

Support Vector ◽

Data Set ◽

Multiple Classification ◽

Learning Machine ◽

Multi Class Classification ◽

The University

Cardiotocography (CTG) is used for monitoring the fetal heart rate signals during pregnancy. Evaluation of these signals by specialists provides information about fetal status. When a clinical decision support system is introduced with a system that can automatically classify these signals, it is more sensitive for experts to examine CTG data. In this study, CTG data were analysed with the Extreme Learning Machine (ELM) algorithm and these data were classified as normal, suspicious and pathological as well as benign and malicious. The proposed method is validated with the University of California International CTG data set. The performance of the proposed method is evaluated with accuracy, f1 score, Cohen kappa, precision, and recall metrics. As a result of the experiments, binary classification accuracy was obtained as 99.29%. There was only 1 false positive. When multi-class classification was performed, the accuracy was obtained as 98.12%. The amount of false positives was found as 2. The processing time of the training and testing of the ELM algorithm were quite minimized in terms of data processing compared to the support vector machine and multi-layer perceptron. This result proved that a high classification accuracy was obtained by analysing the CTG data both binary and multiple classification.

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Extraction of Abandoned Land in Hilly Areas Based on the Spatio-Temporal Fusion of Multi-Source Remote Sensing Images

Remote Sensing ◽

10.3390/rs13193956 ◽

2021 ◽

Vol 13 (19) ◽

pp. 3956

Author(s):

Shan He ◽

Huaiyong Shao ◽

Wei Xian ◽

Shuhui Zhang ◽

Jialong Zhong ◽

...

Keyword(s):

Remote Sensing ◽

Single Type ◽

Support Vector ◽

Svm Classifier ◽

Cultivated Land ◽

Remote Sensing Images ◽

Data Set ◽

Abandoned Land ◽

Spatio Temporal ◽

Sentinel 2

Hilly areas are important parts of the world’s landscape. A marginal phenomenon can be observed in some hilly areas, leading to serious land abandonment. Extracting the spatio-temporal distribution of abandoned land in such hilly areas can protect food security, improve people’s livelihoods, and serve as a tool for a rational land plan. However, mapping the distribution of abandoned land using a single type of remote sensing image is still challenging and problematic due to the fragmentation of such hilly areas and severe cloud pollution. In this study, a new approach by integrating Linear stretch (Ls), Maximum Value Composite (MVC), and Flexible Spatiotemporal DAta Fusion (FSDAF) was proposed to analyze the time-series changes and extract the spatial distribution of abandoned land. MOD09GA, MOD13Q1, and Sentinel-2 were selected as the basis of remote sensing images to fuse a monthly 10 m spatio-temporal data set. Three pieces of vegetation indices (VIs: ndvi, savi, ndwi) were utilized as the measures to identify the abandoned land. A multiple spatio-temporal scales sample database was established, and the Support Vector Machine (SVM) was used to extract abandoned land from cultivated land and woodland. The best extraction result with an overall accuracy of 88.1% was achieved by integrating Ls, MVC, and FSDAF, with the assistance of an SVM classifier. The fused VIs image set transcended the single source method (Sentinel-2) with greater accuracy by a margin of 10.8–23.6% for abandoned land extraction. On the other hand, VIs appeared to contribute positively to extract abandoned land from cultivated land and woodland. This study not only provides technical guidance for the quick acquirement of abandoned land distribution in hilly areas, but it also provides strong data support for the connection of targeted poverty alleviation to rural revitalization.

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ChemTok: A New Rule Based Tokenizer for Chemical Named Entity Recognition

BioMed Research International ◽

10.1155/2016/4248026 ◽

2016 ◽

Vol 2016 ◽

pp. 1-9 ◽

Cited By ~ 5

Author(s):

Abbas Akkasi ◽

Ekrem Varoğlu ◽

Nazife Dimililer

Keyword(s):

Conditional Random Fields ◽

Named Entity Recognition ◽

Classification Performance ◽

Entity Recognition ◽

Support Vector ◽

Learning Approaches ◽

Data Set ◽

Rule Based ◽

Named Entity ◽

Vector Machines

Named Entity Recognition (NER) from text constitutes the first step in many text mining applications. The most important preliminary step for NER systems using machine learning approaches is tokenization where raw text is segmented into tokens. This study proposes an enhanced rule based tokenizer, ChemTok, which utilizes rules extracted mainly from the train data set. The main novelty of ChemTok is the use of the extracted rules in order to merge the tokens split in the previous steps, thus producing longer and more discriminative tokens. ChemTok is compared to the tokenization methods utilized by ChemSpot and tmChem. Support Vector Machines and Conditional Random Fields are employed as the learning algorithms. The experimental results show that the classifiers trained on the output of ChemTok outperforms all classifiers trained on the output of the other two tokenizers in terms of classification performance, and the number of incorrectly segmented entities.

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Effects of Class Purity of Training Patch on Classification Performance of Crop Classification with Convolutional Neural Network

Applied Sciences ◽

10.3390/app10113773 ◽

2020 ◽

Vol 10 (11) ◽

pp. 3773

Author(s):

Soyeon Park ◽

No-Wook Park

Keyword(s):

Classification Accuracy ◽

Supervised Classification ◽

Patch Size ◽

Classification Performance ◽

Spatial Homogeneity ◽

High Class ◽

Large Size ◽

Training Samples ◽

Compositional Homogeneity ◽

Crop Classification

As the performance of supervised classification using convolutional neural networks (CNNs) are affected significantly by training patches, it is necessary to analyze the effects of the information content of training patches in patch-based classification. The objective of this study is to quantitatively investigate the effects of class purity of a training patch on performance of crop classification. Here, class purity that refers to a degree of compositional homogeneity of classes within a training patch is considered as a primary factor for the quantification of information conveyed by training patches. New quantitative indices for class homogeneity and variations of local class homogeneity over the study area are presented to characterize the spatial homogeneity of the study area. Crop classification using 2D-CNN was conducted in two regions (Anbandegi in Korea and Illinois in United States) with distinctive spatial distributions of crops and class homogeneity over the area to highlight the effect of class purity of a training patch. In the Anbandegi region with high class homogeneity, superior classification accuracy was obtained when using large size training patches with high class purity (7.1%p improvement in overall accuracy over classification with the smallest patch size and the lowest class purity). Training patches with high class purity could yield a better identification of homogenous crop parcels. In contrast, using small size training patches with low class purity yielded the highest classification accuracy in the Illinois region with low class homogeneity (19.8%p improvement in overall accuracy over classification with the largest patch size and the highest class purity). Training patches with low class purity could provide useful information for the identification of diverse crop parcels. The results indicate that training samples in patch-based classification should be selected based on the class purity that reflects the local class homogeneity of the study area.

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Multiclass Contour-Preserving Classification with Support Vector Machine (SVM)

Journal of Intelligent Systems ◽

10.1515/jisys-2015-0087 ◽

2017 ◽

Vol 26 (2) ◽

pp. 323-334 ◽

Cited By ~ 1

Author(s):

Piyabute Fuangkhon

Keyword(s):

Support Vector Machine ◽

Classification Accuracy ◽

University Of California ◽

Support Vector ◽

Data Sets ◽

Feed Forward Neural Network ◽

Real World Data ◽

Data Set ◽

The University ◽

Training Sets

AbstractMulticlass contour-preserving classification (MCOV) has been used to preserve the contour of the data set and improve the classification accuracy of a feed-forward neural network. It synthesizes two types of new instances, called fundamental multiclass outpost vector (FMCOV) and additional multiclass outpost vector (AMCOV), in the middle of the decision boundary between consecutive classes of data. This paper presents a comparison on the generalization of an inclusion of FMCOVs, AMCOVs, and both MCOVs on the final training sets with support vector machine (SVM). The experiments were carried out using MATLAB R2015a and LIBSVM v3.20 on seven types of the final training sets generated from each of the synthetic and real-world data sets from the University of California Irvine machine learning repository and the ELENA project. The experimental results confirm that an inclusion of FMCOVs on the final training sets having raw data can improve the SVM classification accuracy significantly.

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A Preliminary Investigation Of Decision Tree Models For Classification Accuracy Rates And Extracting Interpretable Rules In The Credit Scoring Task: A Case Of The German Data Set

Review of Business Information Systems (RBIS) ◽

10.19030/rbis.v12i3.4352 ◽

2008 ◽

Vol 12 (3) ◽

Author(s):

Jozef Zurada ◽

Peng C. Lam

Keyword(s):

Decision Tree ◽

Classification Accuracy ◽

Credit Scoring ◽

Preliminary Investigation ◽

Machine Learning Techniques ◽

Support Vector ◽

Data Set ◽

Consumer Loans ◽

Tree Models ◽

Accuracy Rates

For many years lenders have been using traditional statistical techniques such as logistic regression and discriminant analysis to more precisely distinguish between creditworthy customers who are granted loans and non-creditworthy customers who are denied loans. More recently new machine learning techniques such as neural networks, decision trees, and support vector machines have been successfully employed to classify loan applicants into those who are likely to pay a loan off or default upon a loan. Accurate classification is beneficial to lenders in terms of increased financial profits or reduced losses and to loan applicants who can avoid overcommitment. This paper examines a historical data set from consumer loans issued by a German bank to individuals whom the bank considered to be qualified customers. The data set consists of the financial attributes of each customer and includes a mixture of loans that the customers paid off or defaulted upon. The paper examines and compares the classification accuracy rates of three decision tree techniques as well as analyzes their ability to generate easy to understand rules.

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Sentiment polarity classification of tweets using a extended dictionary

INTELIGENCIA ARTIFICIAL ◽

10.4114/intartif.vol21iss62pp1-12 ◽

2018 ◽

Vol 21 (62) ◽

pp. 1

Author(s):

Jorge E. Camargo ◽

Vladimir Vargas-Calderon ◽

Nelson Vargas ◽

Liliana Calderón-Benavides

Keyword(s):

Support Vector Machine ◽

Classification Accuracy ◽

Classification Performance ◽

Semantic Relations ◽

Support Vector ◽

The Real ◽

Polarity Classification ◽

Real Academia ◽

Word Definitions

With the purpose of classifying text based on its sentiment polarity (positive or negative), we proposed an extension of a 68,000 tweets corpus through the inclusion of word definitions from a dictionary of the Real Academia Espa\~{n}ola de la Lengua (RAE). A set of 28,000 combinations of 6 Word2Vec and support vector machine parameters were considered in order to evaluate how positively would affect the inclusion of a RAE's dictionary definitions classification performance. We found that such a corpus extension significantly improve the classification accuracy. Therefore, we conclude that the inclusion of a RAE's dictionary increases the semantic relations learned by Word2Vec allowing a better classification accuracy.

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Mapping Mangrove Forests of Dongzhaigang Nature Reserve in China Using Landsat 8 and Radarsat-2 Polarimetric SAR Data

Sensors ◽

10.3390/s18114012 ◽

2018 ◽

Vol 18 (11) ◽

pp. 4012 ◽

Cited By ~ 5

Author(s):

Jianing Zhen ◽

Jingjuan Liao ◽

Guozhuang Shen

Keyword(s):

Classification Accuracy ◽

Mangrove Forest ◽

Nature Reserve ◽

Radar Data ◽

Optical Data ◽

Support Vector ◽

Accurate Information ◽

Landsat 8 ◽

Mangrove Forests ◽

Sar Data

Mangrove forests are distributed in intertidal regions that act as a “natural barrier” to the coast. They have enormous ecological, economic, and social value. However, the world’s mangrove forests are declining under immense pressure from anthropogenic and natural disturbances. Accurate information regarding mangrove forests is essential for their protection and restoration. The main objective of this study was to develop a method to improve the classification of mangrove forests using C-band quad-pol Synthetic Aperture Radar (SAR) data (Radarsat-2) and optical data (Landsat 8), and to analyze the spectral and backscattering signatures of mangrove forests. We used a support vector machine (SVM) classification method to classify the land use in Hainan Dongzhaigang National Nature Reserve (HDNNR). The results showed that the overall accuracy using only optical information was 83.5%. Classification accuracy was improved to a varying extent by the addition of different radar data. The highest overall accuracy was 95.0% based on a combination of SAR and optical data. The area of mangrove forest in the reserve was found to be 1981.7 ha, as determined from the group with the highest classification accuracy. Combining optical data with SAR data could improve the classification accuracy and be significant for mangrove forest conservation.

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Improving the classification performance with group lasso-based ranking method in high dimensional correlated data

Journal of Theoretical and Computational Chemistry ◽

10.1142/s021963362040009x ◽

2020 ◽

Vol 19 (03) ◽

pp. 2040009

Author(s):

Abhijeet R Patil ◽

Bong-Jin Choi ◽

Sangjin Kim

Keyword(s):

Classification Accuracy ◽

Geometric Mean ◽

Classification Performance ◽

Group Lasso ◽

Correlated Data ◽

Support Vector ◽

Cpg Sites ◽

Highly Correlated ◽

Selection Operator ◽

Sensitivity Specificity

The high-throughput correlated DNA methylation (DNAmeth) dataset generated from Illumina Infinium Human Methylation 27 (IIHM 27K) BeadChip assay. In the DNAmeth data, there are several CpG sites for every gene, and these grouped CpG sites are highly correlated. Most of the current filtering-based ranking (FBR) methods do not consider the group correlation structures. Obtaining the significant features with the FBR methods and applying these features to the classifiers to attain the best classification accuracy in highly correlated DNAmeth data is a challenging task. In this research, we introduce a resampling of group least absolute shrinkage and selection operator (glasso) FBR method capable of ignoring the unrelated features in the data considering the group correlation among the features. The various classifiers, such as random forests (RF), Naive Bayes (NB), and support vector machines (SVM) with the significant CpGs obtained from the proposed resampling of group lasso-based ranking (RGLR) method helped to boost the classification accuracy. Through simulated and experimental prostate DNAmeth data, we showed that higher performance of accuracy, sensitivity, specificity, and geometric mean is achieved by ignoring the unimportant CpG sites through the RGLR method.

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