Title: Image classification using higher-order statistics-based ICA for NOAA multispectral satellite image
Authors: T. Venkata Krishnamoorthy; G. Umamaheswara Reddy
Addresses: Department of Electronics and Communication Engineering, Sri Venkateswara University College of Engineering, Sri Venkateswara University, Tirupati, Andhra Pradesh, 517502, India ' Department of Electronics and Communication Engineering, Sri Venkateswara University College of Engineering, Sri Venkateswara University, Tirupati, Andhra Pradesh, 517502, India
Abstract: The main objective of this research is an object classification with reduced bands of a multispectral National Oceanic and Atmospheric Administration image by using a higher-order statistics-based independent component analysis and clustering method. Enhancement is not only improving the spatial resolution, but should be preserving the spectral information. The ICA is used for dimensional reduction of multispectral images and enhancement of techniques for improving the spectral and spatial values. This integrated composite image is classified by using a K-means clustering algorithm, and the objects are separated based on homogeneity feature levels with pixel values. This unsupervised classification can be used for extracting land, water and clouds with high accuracy and kappa coefficient values compared with band calibration values of NDVI and surface temperature. The image has low colour distortion, high resolution, improved visual quality, and accurate information with good statistical parameter values.
Keywords: K-means clustering; maximum like hood; kappa coefficient; independent component analysis; ICA; higher-order statistics.
International Journal of Advanced Intelligence Paradigms, 2020 Vol.17 No.1/2, pp.178 - 191
Received: 28 Dec 2017
Accepted: 16 Feb 2018
Published online: 03 Aug 2020 *