In this paper a new supervised classification method for hyperspectral image is introduced. In the proposed method first, 2D non-subsampled shearlet transform is applied to each spectral band of hyperspectral images. After that, minimum noise fraction transform reduces the dimension of shearlet coefficient sub-bands. Finally, the support vector machine is used for classifying the hyperspectral images based on the extracted features. In order to validate the efficiency of the proposed algorithm, two real hyperspectral image datasets are selected. The obtained classification results are compared with some of the state-of-the-art classification algorithms and the proposed method has reached the highest classification accuracy.
Mohamad Reza Soleimanzadeh and Azam Karami, "Hyperspectral image classification using nonsubsampled shearlet transform," Proc. SPIE 10427, Image and Signal Processing for Remote Sensing XXIII, 104270K (Presented at SPIE Remote Sensing: September 12, 2017; Published: 4 October 2017); https://doi.org/10.1117/12.2278064.
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