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Hyperspectral imaging from 820 nm to 1666 nm was used on sound and Fusarium graminearum infected samples of Canadian Western Red Spring Wheat. Samples had moisture contents of 19%, 27% and 35%, and infection level ranging from 0 to 56 days. Genetic algorithm optimization, using information theoretic fitness criterion, reduced the original 256 wavelengths hypercube to a set of only 10 wavelengths....
Support vector machines is a very efficient and frequently used method in classification of hyperspectral images since they provide high classification accuracy even with a limited number of training samples. The accuracy of SVM depends on choice of kernel parameters. In order to obtain a high classification accuracy, it is vital to optimally determine the kernel parameters. In this work, harmony...
Intermediate results of two state-of-the-art wrapper feature selection approaches (GA and SFFS) applied to hyperspectral data sets were used to derive information about band importance for specific land cover classification problems. Several feature selection performance scores (classification accuracies, Bhattacharyya separability) were tested. The impact of the number of selected bands on classification...
Intermediate results of two state-of-the-art wrapper feature selection approaches (GA and SFFS) associated to a classifier (linear SVM) applied to hyperspectral data sets were used to derive information about band importance for specific land cover classification problems. The impact of the number of selected bands on classification accuracy was obtained thanks to SFFS, while a band importance measure...
Selective principal component regression analysis (SPCR) uses a subset of the original image bands for principal component transformation and regression. For optimal band selection before the transformation, this paper used genetic algorithms (GA). In this case, the GA process used the regression correlation as its fitness function. This algorithm was used for analyzing fluorescence hyperspectral...
We propose a new noise reduction algorithm for the denoising of hyperspectral images. The proposed algorithm, Genetic Kernel Tucker Decomposition (GKTD), exploits both the spectral and the spatial information in the images. With respect to a previous approach, we use the kernel trick to apply a Tucker decomposition on a higher dimensional feature space instead of the input space. A genetic algorithm...
This paper presents a new method for hyperspectral image classification. It combines support vector machine (SVM), particle swarm optimization (PSO), and genetic algorithm (GA) together. Its aim is to improve the classification accuracy and reduce the computation consumption based on heuristic algorithms. Because the classification accuracy is impacted by the parameters of the SVM model and feature...
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