Machine Learning
Application of the Karhunen-Loève Expansion to Feature Selection and Ordering
IEEE Transactions on Computers
Bayesian Hyperspectral Image Segmentation with Discriminative Class Learning
IbPRIA '07 Proceedings of the 3rd Iberian conference on Pattern Recognition and Image Analysis, Part I
Mining data with random forests: A survey and results of new tests
Pattern Recognition
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Since hyperspectral imagery (HSI) (or remotely sensed data) provides more information (or additional bands) than traditional gray level and color images, it can be used to improve the performance of image classification applications. A hyperspectral image presents spectral features (also called spectral signature) of regions in the image as well as spatial features. Feature reduction, selection, and transformation has been a challenging problem for hyperspectral image classification due to the high number of dimensions. In this paper, we firstly use Random Forest (RF) algorithm to select significant features and then apply Kernel Fukunaga Koontz Transform (K-FKT), a non-linear statistical technique, for the classification. We provide our experimental results on AVIRIS hyperspectral image dataset that contains various types of field crops. In our experimental results, we have obtained overall classification accuracy around 84 percent for the classification of 16 types of field crops.