Abstract
A new approach of unmixing with the subspace method is proposed and an experiment using hyperspectral image was conducted. In stead of using conventional statistical unmixing procedures which incorporate all channel data to perform unmixing, the proposed approach assigns subspace for each unmixing class. In this method, unmixing is calculated by the projection of observed pixel vector on the class subspaces. This method is more stable than conventional methods against noises and works effectively as a feature extraction and data reduction procedure at the same time. Owing to these advantages, this approach is suitable for the unmixing of hyper spectral image which has high correlation between channels. The performance of this method is tested by an experimented using a hyper spectral airborne casi image acquired over a wetland area. Unmixing of 7 wetland vegetation classes were calculated using least square, quadratic programming, orthogonal subspace projection and subspace method. Finally, the results of unmixing experiment were compared and evaluated for the use of wetland vegetation monitoring.
