On sphering the high resolution satellite image using fixed point based ICA approach

Abstract

On sphering the satellite data, classified images are achieved by many authors that had tried to reduce the mixing effect in image classes with the help of different Independent component analysis (ICA) based approaches. In these cases multispectral images are limited with small spectral variation in heterogeneous classes. For better classification, high spectral variance among different classes and low spectral variance within a particular class should exhibit. In the consideration of this issue, a Fixed point (FP) based Independent Component Analysis (ICA) method is utilized to get better classification accuracy in the existing mixed classes that consist similar spectral behavior. This FP-ICA method identifies the objects from mixed classes having similar spectral characteristics, on sphering high resolution satellite images (HRSI). It also helps to reduce the effect of similar spectral behavior between different image classes. The estimation of independent component related to non-gaussian distribution data (image) with optimizing the performance of this approach with the help of nonlinearity, which utilize the low variance between similar spectral classes. It is quite robust, effortless in computation and high convergence rate, even though the spectral distributions of satellite images are rigid to classify. Hence, this FP-ICA approach plays a key role in image classification such as buildings, grassland area, road, and vegetation. © Springer Science+Business Media Singapore 2017.