Target detection for hyperspectral images using ICA-based feature extraction

In this paper we present a target detection method for hyperspectral images using feature extraction based on Independent Component Analysis (ICA). This method makes good use of the high order statistic of image data and greatly overcome the spectral signature variability. ICA aims to find a linear representation of the observed data in order that the components are statistically independent, or as independent as possible. Such an independent component can capture the intrinsic structure of data and extract image features, including target feature that will be used in detection. First each pixel, which is assumed to be a linear mixture of target and background spectra, is projected onto the orthogonal background subspace to remove the background spectral portion from the corresponding pixel spectrum. Then the targets in the background-removed image are estimated through matched filtering with the feature of target component extracted by ICA. The method has been testified on Airborne Visible and Infrared Imaging Spectrometer (AVIRIS) data. The experimental results show that targets are successfully separated from the background, demonstrating the good performance of this method to detect targets in hyperspectral images.