One of the primary goals of imaging spectrometry in earth remote sensing applications is to determine identities and abundances of surface materials. In a recent study, an orthogonal subspace projection (OSP) was proposed for image classification. However, it was developed for an a priori linear spectral mixture model which did not take advantage of a posteriori knowledge of observations. In this paper, an a posterior least squares orthogonal subspace projection (LSOSP) derived from OSP is presented on the basis of an a posteriori model so that the abundances of signatures can be estimated through observations rather than assumed to be known as in the a priori model. In order to evaluate the OSP and LSOSP approaches, a Neyman-Pearson detection theory is developed where a receiver operating characteristic (ROC) curve is used for performance analysis. In particular, a locally optimal Neyman-Pearson's detector is also designed for the case where the global abundance is very small with energy close to zero a case to which both LSOSP and OSP cannot be applied. It is shown through computer simulations that the presented LSOSP approach significantly improves the performance of OSP.
|Number of pages||13|
|Journal||IEEE Transactions on Geoscience and Remote Sensing|
|Publication status||Published - 1997 Dec 1|
All Science Journal Classification (ASJC) codes
- Electrical and Electronic Engineering
- Earth and Planetary Sciences(all)