Unsupervised Classification of Hyperspectral Images using PCA and K-Means
Keyword(s):
The visualization of hyperspectral images in display devices, having RGB colour composition channels is quite difficult due to the high dimensionality of these images. Thus, principal component analysis has been used as a dimensionality reduction algorithm to reduce information loss, by creating uncorrelated features. To classify regions in the hyperspectral images, K-means clustering has been used to form clusters/regions. These two algorithms have been implemented on the three datasets imaged by AVIRIS and ROSIS sensors.
2021 ◽
Vol XLVI-M-1-2021
◽
pp. 423-427
2015 ◽
Vol 8
(6)
◽
pp. 715-725
2016 ◽
Vol 16
(5)
◽
pp. 1395-1406
◽
2018 ◽
Vol 51
(1)
◽
pp. 375-390
◽
2020 ◽
Vol 537
◽
pp. 012034
2021 ◽
Vol 14
◽
pp. 1233-1245
2017 ◽
Vol 7
(8)
◽
pp. 30