Cluster-space representation for hyperspectral data classification

This paper presents a generalization of the hybrid supervised-unsupervised approach to image classification, and an automatic procedure for implementing it with hyperspectral data. Cluster-space representation is introduced in which clustered training data is displayed in a one-dimensional (I-D) cluster-space showing its probability distribution. This representation leads to automatic association of spectral clusters with information classes and the development of a cluster-space classification (CSC). Pixel labeling is undertaken by a combined decision based on its membership of belonging to defined clusters and the clusters' membership of belonging to information classes. The method provides a means of class data separability inspection, visually and quantitatively, regardless of the number of spectral bands used. The class modeling requires only that first degree statistics be estimated; therefore, the number of training samples required can be many fewer than when using Gaussian maximum likelihood (GML) classification. Experiments are presented based on computer generated data and AVIRIS data. The advantages of the method are demonstrated showing improved capacity for data classification.