Target detection in hyperspectral imagery using noise adjusted principal component analysis and orthogonal subspace projection

Abstract

Orthogonal subspace projection (OSP) has been used in hyperspectral image processing for automatic target detection and image classification. Existing OSP based approaches for target detection require a priori knowledge of all undesired signatures present in the input scene. In this paper, we proposed a new technique for target detection which does not require a priori knowledge of the non-target signatures present in the input scene. The length of any pixel vector containing the target reduces significantly when it is projected in a direction orthogonal to the target signature. Thus the ratio between the original pixel vector to the projected pixel vector yields a high value for the pixels containing the target. Therefore, an OSP based parameter along with noise adjusted principal component analysis (NAPCA) was introduced in this paper for target detection in hyperspectral images. For noisy images, NAPCA is used as a preprocessing step to reduce the effects of noise as well as to reduce the spectral dimension thereby yielding better target detection capability while enhancing the computational efficiency. For noise-free input scenes or when very small amount of noise is present in the input scene, principal component analysis (PCA) may be used instead of NAPCA. The OSP based technique requires that the number of spectrally distinct signatures present in the input scene must be less than the number of spectral bands. The proposed algorithm yields very good results even when this criterion is not satisfied.