Improvements on hyperspectral classification algorithms

Özdemir, Okan Bilge
Çetin, Yasemin
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 training data size and sampling strategy is demonstrated over the high resolution Pavia University hyperspectral data.
5th Workshop on Hyperspectral Image and Signal Processing - Evolution in Remote Sensing (WHISPERS)


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...
The Effect of Training Data on Hyperspectral Classification Algorithms
Özdemir, Okan Bilge; Cetin, Yasemin Yardimci (2013-01-01)
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....
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 new framework of multi-objective evolutionary algorithms for feature selection and multi-label classification of video data
Karagoz, Gizem Nur; Yazıcı, Adnan; Dokeroglu, Tansel; Coşar, Ahmet (2020-06-01)
There are few studies in the literature to address the multi-objective multi-label feature selection for the classification of video data using evolutionary algorithms. Selecting the most appropriate subset of features is a significant problem while maintaining/improving the accuracy of the prediction results. This study proposes a framework of parallel multi-objective Non-dominated Sorting Genetic Algorithms (NSGA-II) for exploring a Pareto set of non-dominated solutions. The subsets of non-dominated featu...
Usability Problem Reports for Comparative Studies: Consistency and Inspectability
Vermeeren, Arnold P. O. S.; Attema, Jelle; Akar, Evren; de Ridder, Huib; van Doorn, Andrea J.; Erbuğ, Çiğdem; Berkman, Ali E.; Maguire, Martin C. (2008-01-01)
This study explores issues of consistency and inspectability in usability test data analysis processes and reports. Problem reports resulting from usability tests performed by three professional usability labs in three different countries are compared. Each of the labs conducted a usability test on the same product, applying an agreed test protocol that was collaboratively developed by the labs. Each lab first analyzed their own findings as they always do in their regular professional practice. A few weeks ...
Citation Formats
O. B. Özdemir and Y. Çetin, “Improvements on hyperspectral classification algorithms,” presented at the 5th Workshop on Hyperspectral Image and Signal Processing - Evolution in Remote Sensing (WHISPERS), Gainesville, FL, 2013, Accessed: 00, 2020. [Online]. Available: