Unsupervised Least Square Unmixing Approach for Subpixel Target Detection on Multispectral Remotely Sensed Imagery

Least square unmixing approach has been successfully applied to hyperspectral remotely sensed images 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 might be greater than the number of bands. In order to relax this constraint, an extension of the least square approach is presented. With a set of least square filters that are nonlinearly combined, endmember detection for multispectral images can be realized. Furthermore, to detect targets in unknown background is a greater challenge. That is the well known Automatic Target Recognition (ATR) programs. In this paper, we also proposed a Multispectral Target Generation Process (MTGP) that will automatic search for potential targets in the image scene. 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