scholarly journals On Appraisal of Spectral Features Based Supervised Classifications for Hyperspectral Images

2020 ◽  
Vol 8 (6) ◽  
pp. 593-600
Keyword(s):  
Spectral Data ◽  
Execution Time ◽  
Supervised Classification ◽  
Performance Metrics ◽  
Hyperspectral Images ◽  
Spectral Features ◽  
Classification Methods ◽  
Spectral Bands ◽  
Supervised Classification Methods

The classification of hyperspectral images is a challenging task since it contains unbalanced ratio between the training and testing samples, and number of spectral bands. The detailed spectral data of hyperspectral images increases the ability to individualize the different classes and achieving accurate classification maps. Hence, in this paper, we use spectral data for classification and we address the performance of different supervised classification techniques like logic-based, ensemble-based, statistical-based, non-probabilistic-based and instance-based techniques on spectral features. Experiments are carried out using hyperspectral imagery captured by AVIRIS sensor such as Indian Pines, Salinas and Salinas-A. The appraisal of these supervised classification methods are held with each other in terms of performance metrics such as overall accuracy, precision, recall, F1-score and execution time.

scholarly journals Classification of Hyperspectral Remote Sensing for Production Minerals Mapping Using Geological Map and Geomatics Techniques

2018 ◽  
Vol 7 (4.20) ◽  
pp. 480 ◽  
Author(s):  
Hussein Sabah Jaber ◽  
. .
Keyword(s):  
Remote Sensing ◽  
Spectral Data ◽  
Hyperspectral Images ◽  
Band Ratio ◽  
Multispectral Data ◽  
Spectral Bands ◽  
Geological Map ◽  
Aster Image ◽  
Classification Procedures

The classification of hyperspectral images is an interesting job since the data dimension is huge for conventional classification procedures; normally several hundreds of spectral bands are attained for each image. These spectral bands can supported very rich spectral data of each pixel to find objects material .The objective of this research is to classify hyperspectral images for detection and production of detailed minerals mapping using geological map and Environment for Visualizing Images (ENVI) software. In this research, ASTER data and geological map have been used. Some techniques on these data are used such as enhancement, matching (linking), De-correlation, Band Ratio, stacking image and classification. The results showed that comparison of the two classification results showed the classification of stack image with the aspect and the slope provide more information than classification of ASTER image alone. Also, using ENVI software to generate 3D surface views.It concluded that capability of hyperspectral and its differentiation with multispectral data to extract detailed features from ASTER image. 

Comparison of Supervised Classification Methods for Protein Profiling in Cancer Diagnosis

2007 ◽  
Vol 3 ◽  
pp. 117693510700300 ◽  
Author(s):  
Nadège Dossat ◽  
Alain Mangé ◽  
Jérôme Solassol ◽  
William Jacot ◽  
Ludovic Lhermitte ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Supervised Classification ◽  
Protein Profiling ◽  
Clinical Proteomics ◽  
High Dimensional ◽  
Classification Methods ◽  
Linear Discriminant ◽  
Supervised Classification Methods

A key challenge in clinical proteomics of cancer is the identification of biomarkers that could allow detection, diagnosis and prognosis of the diseases. Recent advances in mass spectrometry and proteomic instrumentations offer unique chance to rapidly identify these markers. These advances pose considerable challenges, similar to those created by microarray-based investigation, for the discovery of pattern of markers from high-dimensional data, specific to each pathologic state (e.g. normal vs cancer). We propose a three-step strategy to select important markers from high-dimensional mass spectrometry data using surface enhanced laser desorption/ionization (SELDI) technology. The first two steps are the selection of the most discriminating biomarkers with a construction of different classifiers. Finally, we compare and validate their performance and robustness using different supervised classification methods such as Support Vector Machine, Linear Discriminant Analysis, Quadratic Discriminant Analysis, Neural Networks, Classification Trees and Boosting Trees. We show that the proposed method is suitable for analysing high-throughput proteomics data and that the combination of logistic regression and Linear Discriminant Analysis outperform other methods tested.

A Comparison of Semi-Supervised Classification Approaches for Software Defect Prediction

2014 ◽  
Vol 23 (1) ◽  
pp. 75-82 ◽  
Author(s):  
Cagatay Catal
Keyword(s):  
Supervised Classification ◽  
Defect Prediction ◽  
Support Vector ◽  
Software Defect Prediction ◽  
Classification Methods ◽  
Data Set ◽  
Software Defect ◽  
Data Points ◽  
Supervised Classification Methods ◽  
Prediction Approach

AbstractPredicting the defect-prone modules when the previous defect labels of modules are limited is a challenging problem encountered in the software industry. Supervised classification approaches cannot build high-performance prediction models with few defect data, leading to the need for new methods, techniques, and tools. One solution is to combine labeled data points with unlabeled data points during learning phase. Semi-supervised classification methods use not only labeled data points but also unlabeled ones to improve the generalization capability. In this study, we evaluated four semi-supervised classification methods for semi-supervised defect prediction. Low-density separation (LDS), support vector machine (SVM), expectation-maximization (EM-SEMI), and class mass normalization (CMN) methods have been investigated on NASA data sets, which are CM1, KC1, KC2, and PC1. Experimental results showed that SVM and LDS algorithms outperform CMN and EM-SEMI algorithms. In addition, LDS algorithm performs much better than SVM when the data set is large. In this study, the LDS-based prediction approach is suggested for software defect prediction when there are limited fault data.

scholarly journals Overview of Hyperspectral Image Classification

2020 ◽  
Vol 2020 ◽  
pp. 1-13 ◽  
Author(s):  
Wenjing Lv ◽  
Xiaofei Wang
Keyword(s):  
Supervised Classification ◽  
Hyperspectral Image ◽  
Unsupervised Classification ◽  
Hyperspectral Images ◽  
Hyperspectral Image Classification ◽  
Remote Sensing Technology ◽  
Sensing Technology ◽  
Good Classification ◽  
Semisupervised Classification

With the development of remote sensing technology, the application of hyperspectral images is becoming more and more widespread. The accurate classification of ground features through hyperspectral images is an important research content and has attracted widespread attention. Many methods have achieved good classification results in the classification of hyperspectral images. This paper reviews the classification methods of hyperspectral images from three aspects: supervised classification, semisupervised classification, and unsupervised classification.

Kernel Low-Rank and Sparse Graph for Unsupervised and Semi-Supervised Classification of Hyperspectral Images

2016 ◽  
Vol 54 (6) ◽  
pp. 3410-3420 ◽  
Author(s):  
Frank de Morsier ◽  
Maurice Borgeaud ◽  
Volker Gass ◽  
Jean-Philippe Thiran ◽  
Devis Tuia
Keyword(s):  
Supervised Classification ◽  
Hyperspectral Images ◽  
Low Rank ◽  
Sparse Graph

scholarly journals Semi-Supervised Classification of Hyperspectral Images Based on Extended Label Propagation and Rolling Guidance Filtering

2018 ◽  
Vol 10 (4) ◽  
pp. 515 ◽  
Author(s):  
Binge Cui ◽  
Xiaoyun Xie ◽  
Siyuan Hao ◽  
Jiandi Cui ◽  
Yan Lu
Keyword(s):  
Supervised Classification ◽  
Label Propagation ◽  
Hyperspectral Images
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