Abstract:

Lack of spatial resolution in hyperspectral images can cause high false alarm rate in anomaly detection. Aiming at this problem, we present a new anomaly detection algorithm. Firstly, the main components of the low resolution hyperspectral images are extracted by the principal component analysis (PCA) method. Then the extracted principal components and highresolution images are converted using the IHS transform, and the intensity of each component is obtained. Taking its advantage of reversibility, the IHS inverse transform is performed on the new strength of hyperspectral data components, the colors of the components H and the saturation component S, thus the hyperspectral image data with spatial information enhancement is obtained. Finally the KwRX algorithm is used for detecting the anomaly of hyperspectral images. Simulation experiments show that the proposed algorithm outperforms the KRX algorithm and the PCAKRX algorithm, in the number of target pixels and the number of false alarms thus demonstrating its effectiveness and feasibility.

Key words: hyperspectral image fusion;anomaly detection;PCA;IHS;KRX algorithm