Hyperspectral Data Processing: Algorithm Design and Analysis

30.7 Conclusions

This chapter presents a new application of fully abundance-constrained LSMA to subpixel target size estimation. The idea is to apply an unsupervised target detection algorithm, ATGP, to find subpixel targets of interest, and then implement FCLS to estimate the abundance fractions of subpixel targets present in the image, and finally use the obtained abundance fractions to calculate their sizes. For such an approach to be effective, an accurate estimate of abundance fraction for a subpixel target is required. In this case, a fully constrained abundance LSMA such as FCLS is implemented for this purpose. Despite that abundance-constrained linear unmixing has been studied extensively for material quantification, the issue of subpixel target size estimation investigated in this chapter has never been explored in the past. Four LSMA methods are used for validation, which are an unconstrained method, LSOSP, two partially constrained least-squares methods, SCLS, NCLS, and a fully constrained method, FCLS. As demonstrated in simulated and real image experiments, the need of the fully abundance-constrained methods is evident when the target size is smaller than GSD. The target estimation error is increased as the target size is decreased. In addition to subpixel target size estimation this chapter also explores another application, the problem of concealed target detection, and further develops a computer automated method for detecting and classifying unknown concealed targets, ...