Least square approach for subpixel target detection on multispectral remotely sensed imagery

Least square unmixing approach has been successfully applied in hyperspectral image processing for subpixel target detection. It can detect target with size less than a pixel by estimating its abundance fraction resident in each pixel. In order for the this approach to be effective, the number of bands must be larger 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 inherent constraint is not an issue for hyperspectral images since they generally have hundreds of bands, which is more than the number of signatures resident within images. However, this may not be true for multispectral images where the number of signatures to be classified is greater than the number of bands. This paper presents an extension of the least square approach that relaxes this constraint with a set of least square filters that are nonlinearly combined for endmember detection. The effectiveness of the proposed method is evaluated by SPOT images. The experimental results show significantly improves in classification performance than Orthogonal Subspace Projection (OSP).