Method for Unsupervised Remote Imagery Classification Based on Independent Component Analysis

This paper introduces a linear spectral random mixture analysis model based on ICA. It assumes that the spectral signature of an image pixel is linearly mixed by the spectral signature of independent materials. Experimental results with real imagery data show that the method is effective in multi\|spectral remote sensing image classification.