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In real-world application, especially in remote sensing based on image
processing hyperspectral imaging (HSI) shows promising results. Superpixel-based
image segmentation is the powerful tool in hyperspectral image processing. Series
of neighboring pixels composes superpixel which may belong to different classes

but can be regarded as homogenous region. Extraction of more representative fea-
ture is considered to be most important thing in hyperspectral imaging. Training and

testing samples that are more representative are found by proposing a new method
for selecting two k values for representing optimal superpixels. This paper starts

with superpixel shifting as first step and followed by KNN classifier. Which is per-
formed by pixels with minimal spectral features in HSI are clustered together in

the same superpixel. Followed by spatial-spectral feature is extraction by a domain
transformation from spectral to spatial. For each superpixel, training and test samplesare selected to eliminate classification within the same class. An average distancebetween test and training samples are used for determining class label. Finally, by the
results from most common hyperspectral images Indian pines, Salinas, Pavia showthat this method shows a better classification performance.