Hyperspectral Image Classification Using Functional Data Analysis
Hyperspectral Image Classification Using Functional Data Analysis is a scholarly work by Guangrun Xiao, published in 2014 in ''IEEE Transactions on Cybernetics''. The main subjects of the publication include breath test, pixel, image, pattern recognition, perspective, remote sensing, hyperspectral imaging, Full spectral imaging, artificial intelligence, computer science, data analysis, principal component analysis, image classification, and computer vision. Experimental results on three hyperspectral images show that the proposed method can achieve higher classification accuracies in comparison to some state-of-the-art hyperspectral image classification methods.