The Effect of Training Data on Hyperspectral Classification Algorithms

2013-01-01
Özdemir, Okan Bilge
Cetin, Yasemin Yardimci
In this study, the performance of different hyperspectral classification algorithms with the same training set is investigated. In addition, the effect of the dimension and sampling strategy for the training set selection is demonstrated. Support Vector Machines (SVM), K-Nearest Neighbor (K-NN) and Maximum Likelihood (ML) methods are used. The contribution of using spatial information with spectral information is observed. Meanshift segmentation and window weighting methods are used for spatial information. High resolution Pavia University hyperspectral data and Indian Pines data are used in this study.

Suggestions

Improvement of Hyperspectral Classification Accuracy with Limited Training Data Using Meanshift Segmentation
Özdemir, Okan Bilge; Çetin, Yasemin (2014-04-25)
In this study, the performance of hyperspectral classification algorithms with limited training data investigated. Support Vector Machines (SVM) with Gaussian kernel is used. Principle Component Analysis (PCA) is employed for preprocessing and meanshift segmentation is used to incorporate spatial information with spectral information to observe the effect spatial information. Pattern search algorithm is used to optimize meanshift segmentation parameters. The performance of the algorithm is demonstrated on h...
Improvements on hyperspectral classification algorithms
Özdemir, Okan Bilge; Çetin, Yasemin (2013-06-28)
This study investigates the effect of training set selection strategy on classification accuracy of hyperspectral images. This effect is analyzed in conjunction with three other factors, namely the use principal component analysis on the input data, and the use of spatial information and choice of classifier. Support Vector Machines (SVM) and Maximum Likelihood (ML) classifiers are used for demonstration. Meanshift segmentation and majority voting are used for inclusion of spatial information. The effect of...
The Effects of Test Length and Sample Size on Item Parameters in Item Response Theory
Sahin, Alper; ANIL, DUYGU (2017-02-01)
This study investigates the effects of sample size and test length on item-parameter estimation in test development utilizing three unidimensional dichotomous models of item response theory (IRT). For this purpose, a real language test comprised of 50 items was administered to 6,288 students. Data from this test was used to obtain data sets of three test lengths (10, 20, and 30 items) and nine different sample sizes (150, 250, 350, 500, 750, 1,000, 2,000, 3,000 and 5,000 examinees). These data sets were the...
Effects of Content Balancing and Item Selection Method on Ability Estimation in Computerized Adaptive Tests
Sahin, Alper; ÖZBAŞI, DURMUŞ (2017-01-01)
Purpose: This study aims to reveal effects of content balancing and item selection method on ability estimation in computerized adaptive tests by comparing Fisher's maximum information (FMI) and likelihood weighted information (LWI) methods. Research Methods: Four groups of examinees (250, 500, 750, 1000) and a bank of 500 items with 10 different content domains were generated through Monte Carlo simulations. Examinee ability was estimated by fixing all settings except for the item selection methods mention...
A simulation study on the comparison of methods for the analysis of longitudinal count data
İnan, Gül; İlk Dağ, Özlem; Department of Statistics (2009)
The longitudinal feature of measurements and counting process of responses motivate the regression models for longitudinal count data (LCD) to take into account the phenomenons such as within-subject association and overdispersion. One common problem in longitudinal studies is the missing data problem, which adds additional difficulties into the analysis. The missingness can be handled with missing data techniques. However, the amount of missingness in the data and the missingness mechanism that the data ha...
Citation Formats
O. B. Özdemir and Y. Y. Cetin, “The Effect of Training Data on Hyperspectral Classification Algorithms,” presented at the 21st Signal Processing and Communications Applications Conference (SIU), CYPRUS, 2013, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/57400.