Signature reduction methods for target detection in multispectral remote sensing imagery

Multispectral sensors are still widely used in satellite remote sensing. They usually have spectral bands less than ten channels. The problem for so few channels is that it can not directly solve linear mixture model by least square unmixing for subpixel target detection. In order for least square approach to be effective, the number of bands must be greater than or equal to that of signatures to be classified, i.e., the number of equations should be no less than the number of unknowns. This ensures that there are sufficient dimensions to accommodate orthogonal projections resulting from the individual signatures. It is known as band number constraint (BNC). Such constraint is not an issue for hyperspectral images since they generally have hundreds of bands, however, this may not be true for multispectral images where the number of signatures to be classified might be greater than the number of bands. In order to relax this constraint, we present two signature reduction methods to reduce the number of unknowns, based on signature selection and signature fusion. A SPOT image scene will be used for experiment to demonstrate the performance.