Realtime online unsupervised detection and classification for remotely sensed imagery

Realtime online processing is important to provide immediate data analysis for resolving critical situations in real applications of hyperspectral imaging. We have developed a Constrained Linear Discriminant Analysis (CLDA) algorithm, an excellent approach to hyperspectral image classification, and investigated its realtime online implementation. Because the required prior object spectral signatures may be unavailable in practice, we propose its unsupervised version in this paper. The new algorithm includes unsupervised signature estimation in realtime followed by realtime CLDA algorithm for classification. The unsupervised signature estimation is based on linear mixture model and least squares error criterion. The preliminary result using an HYDICE scene demonstrates its feasibility and effectiveness.