Abstract
Explosive growth of applications in hyperspectral image (HSI) has made HSI classification a hot topic in the remote sensing community. The key to improve classification accuracy is how to make full use of the spectral and spatial information. We combine k-nearest neighbor (KNN) algorithm with guided filter which can extract spatial context information and denoise the classification results by edge-preserving filtering. To solve the problem of dimension disaster, we also take dimensionality reduction into account for HSI classification. To verify the feasibility of our proposed methods, we evaluate the performance over four widely used hyperspectral data sets. The experimental results show that with only 5% of samples, our method obtained better performance than improved support vector machine and KNN methods.
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