Matched shrunken subspace detectors for hyperspectral target detection

摘要

AbstractIn this paper, we propose a new approach, called the matched shrunken subspace detector (MSSD), to target detection from hyperspectral images. The MSSD is developed by shrinking the abundance vectors of the target and background subspaces in the hypothesis models of the matched subspace detector (MSD), a popular subspace-based approach to target detection. The shrinkage is achieved by introducing simplel2-norm regularisation (also known as ridge regression or Tikhonov regularisation). We develop two types of MSSD, one with isotropic shrinkage and termed MSSD-i and the other with anisotropic shrinkage and termed MSSD-a. For these two new methods, we provide both the frequentist and Bayesian derivations. Experiments on a real hyperspectral imaging dataset called Hymap demonstrate that the proposed MSSD methods can outperform the original MSD for hyperspectral target detection.