A comparison of support vector machine with maximum likelihood classification algorithms on texture features

2005 ◽  
Author(s):  
Jin Shuying ◽  
Li Deren ◽  
Wang Jingwen
Keyword(s):  
Support Vector Machine ◽  
Maximum Likelihood ◽  
Texture Features ◽  
Support Vector ◽  
Classification Algorithms ◽  
Maximum Likelihood Classification

Detection of magnetite in the Roossenekal area of the Eastern Bushveld Complex, South Africa, using multispectral remote sensing data

2020 ◽  
Vol 123 (4) ◽  
pp. 573-586
Author(s):  
M. Twala ◽  
R. J. Roberts ◽  
C. Munghemezulu
Keyword(s):  
Remote Sensing ◽  
Support Vector Machine ◽  
Maximum Likelihood ◽  
Bushveld Complex ◽  
Support Vector ◽  
Classification Algorithms ◽  
Error Matrix ◽  
Geological Processes ◽  
Multispectral Remote Sensing ◽  
Eastern Limb

Abstract Multispectral sensors, along with common and advanced algorithms, have become efficient tools for routine lithological discrimination and mineral potential mapping. It is with this paradigm in mind that this paper sought to evaluate and discuss the detection and mapping of magnetite on the Eastern Limb of the Bushveld Complex, using high spectral resolution multispectral remote sensing imagery and GIS techniques. Despite the wide distribution of magnetite, its economic importance, and its potential as an indicator of many important geological processes, not many studies had looked at the detection and exploration of magnetite using remote sensing in this region. The Maximum Likelihood and Support Vector Machine classification algorithms were assessed for their respective ability to detect and map magnetite using the PlanetScope Analytic data. A K-fold cross-validation analysis was used to measure the performance of the training as well as the test data. For each classification algorithm, a thematic landcover map was created and an error matrix, depicting the user’s and producer’s accuracies as well as kappa statistics, was derived. A pairwise comparison test of the image classification algorithms was conducted to determine whether the two classification algorithms were significantly different from each other. The Maximum Likelihood Classifier significantly outperformed the Support Vector Machine algorithm, achieving an overall classification accuracy of 84.58% and an overall kappa value of 0.79. Magnetite was accurately discriminated from the other thematic landcover classes with a user’s accuracy of 76.41% and a producer’s accuracy of 88.66%. The overall results of this study illustrated that remote sensing techniques are effective instruments for geological mapping and mineral investigation, especially iron oxide mineralization in the Eastern Limb of the Bushveld Complex.

scholarly journals Evaluación de cuatro algoritmos de clasificación de imágenes satelitales Landsat-8 y Sentinel-2 para la identificación de cobertura boscosa en paisajes altamente fragmentados en Costa Rica

2020 ◽  
pp. 37
Author(s):  
I.D. Ávila-Pérez ◽  
E. Ortiz-Malavassi ◽  
C. Soto-Montoya ◽  
Y. Vargas-Solano ◽  
H. Aguilar-Arias ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Costa Rica ◽  
Maximum Likelihood ◽  
Forest Cover ◽  
Support Vector ◽  
Classification Algorithms ◽  
Landsat 8 ◽  
Classification Methods ◽  
Global Accuracy ◽  
Sentinel 2

<p>Mapping of land use and forest cover and assessing their changes is essential in the design of strategies to manage and preserve the natural resources of a country, and remote sensing have been extensively used with this purpose. By comparing four classification algorithms and two types of satellite images, the objective of the research was to identify the type of algorithm and satellite image that allows higher global accuracy in the identification of forest cover in highly fragmented landscapes. The study included a treatment arrangement with three factors and six randomly selected blocks within the Huetar Norte Zone in Costa Rica. More accurate results were obtained for classifications based on Sentinel-2 images compared to Landsat-8 images. The best classification algorithms were Maximum Likelihood, Support Vector Machine or Neural Networks, and they yield better results than Minimum Distance Classification. There was no interaction among image type and classification methods, therefore, Sentinel-2 images can be used with any of the three best algorithms, but the best result was the combination of Sentinel-2 and Support Vector Machine. An additional factor included in the study was the image acquisition date. There were significant differences among months during which the image was acquired and an interaction between the classification algorithm and this factor was detected. The best results correspond to images obtained in April, and the lower to September, month that corresponds with the period of higher rainfall in the region studied. The higher global accuracy identifying forest cover is obtained with Sentinel-2 images from the dry season in combination with Maximum Likelihood, Support Vector Machine, and Neural Network image classification methods.</p>

Differential Diagnosis of Malignant Thyroid Calcification Nodule Based on Computed Tomography Image Texture

2021 ◽  
Vol 11 (3) ◽  
pp. 767-772
Author(s):  
Wenxian Peng ◽  
Yijia Qian ◽  
Yingying Shi ◽  
Shuyun Chen ◽  
Kexin Chen ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Support Vector Machine ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Ensemble Learning ◽  
Logistic Regression Analysis ◽  
Texture Features ◽  
Support Vector ◽  
Classification Algorithms ◽  
Feature Optimization

Purpose: Calcification nodules in thyroid can be found in thyroid disease. Current clinical computed tomography systems can be used to detect calcification nodules. Our aim is to identify the nature of thyroid calcification nodule based on plain CT images. Method: Sixty-three patients (36 benign and 27 malignant nodules) found thyroid calcification nodules were retrospectively analyzed, together with computed tomography images and pathology finding. The regions of interest (ROI) of 6464 pixels containing calcification nodules were manually delineated by radiologists in CT plain images. We extracted thirty-one texture features from each ROI. And nineteen texture features were picked up after feature optimization by logistic regression analysis. All the texture features were normalized to [0, 1]. Four classification algorithms, including ensemble learning, support vector machine, K-nearest neighbor, decision tree, were used as classification algorithms to identity the benign and malignant nodule. Accuracy, PPV, NPV, SEN, and AUC were calculated to evaluate the performance of different classifiers. Results: Nineteen texture features were selected after feature optimization by logistic regression analysis (P <0.05). Both Ensemble Learning and Support Vector Machine achieved the highest accuracy of 97.1%. The PPV, NPV, SEN, and SPC are 96.9%, 97.4%, 98.4%, and 95.0%, respectively. The AUC was 1. Conclusion: Texture features extracted from calcification nodules could be used as biomarkers to identify benign or malignant thyroid calcification.

Thermography Based Breast Cancer Detection Using Texture Features and Support Vector Machine

2010 ◽  
Vol 36 (3) ◽  
pp. 1503-1510 ◽  
Author(s):  
U. Rajendra Acharya ◽  
E. Y. K. Ng ◽  
Jen-Hong Tan ◽  
S. Vinitha Sree
Keyword(s):  
Breast Cancer ◽  
Support Vector Machine ◽  
Cancer Detection ◽  
Texture Features ◽  
Breast Cancer Detection ◽  
Support Vector

scholarly journals A Comparative Study of Support Vector Machine and Maximum Likelihood Classification to Extract Land Cover of Lahore District, Punjab, Pakistan

2021 ◽  
Vol 64 (3) ◽  
pp. 265-274
Author(s):  
Fatima Mushtaq ◽  
Khalid Mahmood ◽  
Mohammad Chaudhry Hamid ◽  
Rahat Tufail
Keyword(s):  
Support Vector Machine ◽  
Maximum Likelihood ◽  
Land Cover ◽  
Support Vector ◽  
Kappa Statistics ◽  
Error Matrix ◽  
Classification Techniques ◽  
Machine Learning Classification ◽  
Hyper Plane ◽  
Better Than

The advent of technological era, the scientists and researchers develop machine learning classification techniques to classify land cover accurately. Researches prove that these classification techniques perform better than previous traditional techniques. In this research main objective is to identify suitable land cover classification method to extract land cover information of Lahore district. Two supervised classification techniques i.e., Maximum Likelihood Classifier (MLC) (based on neighbourhood function) and Support Vector Machine (SVM) (based on optimal hyper-plane function) are compared by using Sentinel-2 data. For this optimization, four land cover classes have been selected. Field based training samples have been collected and prepared through a survey of the study area at four spatial levels. Accuracy for each of the classifier has been assessed using error matrix and kappa statistics. Results show that SVM performs better than MLC. Overall accuracies of SVM and MLC are 95.20% and 88.80% whereas their kappa co-efficient are 0.93 and 0.84 respectively.  

scholarly journals Analyzing Navigational Data and Predicting Student Grades Using Support Vector Machine

2020 ◽  
Vol 4 (4) ◽  
pp. 243-252
Author(s):  
SriUdaya Damuluri ◽  
Khondkar Islam ◽  
Pouyan Ahmadi ◽  
Namra Shafiq Qureshi
Keyword(s):  
Support Vector Machine ◽  
Information Science ◽  
Research Study ◽  
Educational Institutions ◽  
Support Vector ◽  
Classification Algorithms ◽  
Student Access ◽  
Final Grades ◽  
Student Grades ◽  
Access Patterns

The advent of Learning Management System (LMS) has unfolded a unique opportunity to predict student grades well in advance which benefits both students and educational institutions. The objective of this study is to investigate student access patterns and navigational data of Blackboard (Bb), a form of LMS, to forecast final grades. This research study consists of students who are pursuing a Networking course in Information Science and Technology Department (IST) at George Mason University (GMU). The gathered data consists of a wide variety of attributes, such as the amount of time spent on lecture slides and other learning materials, number of times course contents are accessed, time and days of the week study material is reviewed, and student grades in various assessments. By analyzing these predictors using Support Vector Machine, one of the most efficient classification algorithms available, we are able to project final grades of students and identify those individuals who are at risk for failing the course so that they can receive proper guidance from instructors. After comparing actual grades with predicted grades, it is concluded that our developed model is able to accurately predict grades of 70% of the students. This study stands unique as it is the first to employ solely online LMS data to successfully deduce academic outcomes of students.

Automatic Fruit Disease Classification Using Images

2014 ◽  
pp. 82-100 ◽  
Author(s):  
Shiv Ram Dubey ◽  
Anand Singh Jalal
Keyword(s):  
Support Vector Machine ◽  
Apple Scab ◽  
Texture Features ◽  
Disease Classification ◽  
Economic Losses ◽  
Support Vector ◽  
Test Case ◽  
Agricultural Industry ◽  
Fruit Disease

Diseases in fruit cause devastating problems in economic losses and production in the agricultural industry worldwide. In this chapter, a method to detect and classify fruit diseases automatically is proposed and experimentally validated. The image processing-based proposed approach is composed of the following main steps: in the first step K-Means clustering technique is used for the defect segmentation, in the second step some color and texture features are extracted from the segmented defected part, and finally diseases are classified into one of the classes by using a multi-class Support Vector Machine. The authors have considered diseases of apple as a test case and evaluated the approach for three types of apple diseases, namely apple scab, apple blotch, and apple rot, along with normal apples. The experimental results express that the proposed solution can significantly support accurate detection and automatic classification of fruit diseases. The classification accuracy for the proposed approach is achieved up to 93% using textural information and multi-class support vector machine.

Sign in / Sign up

Export Citation Format

Share Document